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1.

電子ブック
ＥＢ
1. Representation and Management of Narrative Information
Gian Piero Zarri
出版情報: Springer eBooks Computer Science , Springer London, 2009
所蔵情報: loading…
目次情報: 続きを見る
Basic Principles / 1：
Narrative Information in an NKRL Context / 1.1：
Narratology and NKRL / 1.1.1：
The Notion of "Event" in an NKRL Context / 1.1.2：
Knowledge Representation and NKRL / 1.2：
"Standard" Ontologies and the "n-ary" Problem / 1.2.1：
A Plain "n-ary" Solution and Some Related Problems / 1.2.2：
In the Guise of Winding Up / 1.3：
The Knowledge Representation Strategy / 2：
Architecture of NKRL: the Four "Components" / 2.1：
The Data Structures of the Four Components / 2.2：
Definitional/Enumerative Data Structures / 2.2.1：
Descriptive/Factual Data Structures / 2.2.2：
Second-order Structures / 2.3：
The Completive Construction / 2.3.1：
Binding Occurrences / 2.3.2：
The Semantic and Ontological Contents / 2.4：
The Organization of the HClass Hierarchy / 3.1：
General Notions about Ontologies / 3.1.1：
HClass Architecture / 3.1.2：
The Organization of the HTemp Hierarchy / 3.2：
Recent Examples of "Structured" Ontological Systems / 3.2.1：
Main Features of Some Specific HTemp Structures / 3.2.2：
The Query and Inference Procedures / 3.3：
"Search Patterns" and Low-level Inferences / 4.1：
The Algorithmic Structure of Fum / 4.1.1：
Temporal Information and Indexing / 4.1.2：
High-level Inference Procedures / 4.2：
General Remarks about Some Reasoning Paradigms / 4.2.1：
Hypothesis Rules / 4.2.2：
Transformation Rules / 4.2.3：
Integrating the Two Main Inferencing Modes of NKRL / 4.2.4：
Inference Rules and Internet Filtering / 4.2.5：
Conclusion / 4.3：
Technological Enhancements / 5.1：
Theoretical Enhancements / 5.2：
Appendix A
Appendix B
References
Index
Basic Principles / 1：
Narrative Information in an NKRL Context / 1.1：
Narratology and NKRL / 1.1.1：
2.

電子ブック
ＥＢ
2. Representation and Management of Narrative Information
Gian Piero Zarri
出版情報: SpringerLink Books - AutoHoldings , Springer London, 2009
所蔵情報: loading…
目次情報: 続きを見る
Basic Principles / 1：
Narrative Information in an NKRL Context / 1.1：
Narratology and NKRL / 1.1.1：
The Notion of "Event" in an NKRL Context / 1.1.2：
Knowledge Representation and NKRL / 1.2：
"Standard" Ontologies and the "n-ary" Problem / 1.2.1：
A Plain "n-ary" Solution and Some Related Problems / 1.2.2：
In the Guise of Winding Up / 1.3：
The Knowledge Representation Strategy / 2：
Architecture of NKRL: the Four "Components" / 2.1：
The Data Structures of the Four Components / 2.2：
Definitional/Enumerative Data Structures / 2.2.1：
Descriptive/Factual Data Structures / 2.2.2：
Second-order Structures / 2.3：
The Completive Construction / 2.3.1：
Binding Occurrences / 2.3.2：
The Semantic and Ontological Contents / 2.4：
The Organization of the HClass Hierarchy / 3.1：
General Notions about Ontologies / 3.1.1：
HClass Architecture / 3.1.2：
The Organization of the HTemp Hierarchy / 3.2：
Recent Examples of "Structured" Ontological Systems / 3.2.1：
Main Features of Some Specific HTemp Structures / 3.2.2：
The Query and Inference Procedures / 3.3：
"Search Patterns" and Low-level Inferences / 4.1：
The Algorithmic Structure of Fum / 4.1.1：
Temporal Information and Indexing / 4.1.2：
High-level Inference Procedures / 4.2：
General Remarks about Some Reasoning Paradigms / 4.2.1：
Hypothesis Rules / 4.2.2：
Transformation Rules / 4.2.3：
Integrating the Two Main Inferencing Modes of NKRL / 4.2.4：
Inference Rules and Internet Filtering / 4.2.5：
Conclusion / 4.3：
Technological Enhancements / 5.1：
Theoretical Enhancements / 5.2：
Appendix A
Appendix B
References
Index
Basic Principles / 1：
Narrative Information in an NKRL Context / 1.1：
Narratology and NKRL / 1.1.1：
3.

電子ブック
ＥＢ
3. Motivated Reinforcement Learning
Kathryn E. Merrick, Mary Lou Maher
出版情報: Springer eBooks Computer Science , Springer Berlin Heidelberg, 2009
所蔵情報: loading…
目次情報: 続きを見る
Non-Player Characters and Reinforcement Learning / Part I：
Non-Player Characters in Multiuser Games / 1：
Types of Multiuser Games / 1.1：
Massively Multiplayer Online Role-Playing Games / 1.1.1：
Multiuser Simulation Games / 1.1.2：
Open-Ended Virtual Worlds / 1.1.3：
Character Roles in Multiuser Games / 1.2：
Existing Artificial Intelligence Techniques for Non-Player Characters in Multiuser Games / 1.3：
Reflexive Agents / 1.3.1：
Learning Agents / 1.3.2：
Evolutionary Agents / 1.3.3：
Smart Terrain / 1.3.4：
Summary / 1.4：
References / 1.5：
Motivation in Natural and Artificial Agents / 2：
Defining Motivation / 2.1：
Biological Theories of Motivation / 2.2：
Drive Theory / 2.2.1：
Motivational State Theory / 2.2.2：
Arousal / 2.2.3：
Cognitive Theories of Motivation / 2.3：
Curiosity / 2.3.1：
Operant Theory / 2.3.2：
Incentive / 2.3.3：
Achievement Motivation / 2.3.4：
Attribution Theory / 2.3.5：
Intrinsic Motivation / 2.3.6：
Social Theories of Motivation / 2.4：
Conformity / 2.4.1：
Cultural Effect / 2.4.2：
Evolution / 2.4.3：
Combined Motivation Theories / 2.5：
Maslow's Hierarchy of Needs / 2.5.1：
Existence Relatedness Growth Theory / 2.5.2：
Towards Motivated Reinforcement Learning / 2.6：
Defining Reinforcement Learning / 3.1：
Dynamic Programming / 3.1.1：
Monte Carlo Methods / 3.1.2：
Temporal Difference Learning / 3.1.3：
Reinforcement Learning in Complex Environments / 3.2：
Partially Observable Environments / 3.2.1：
Function Approximation / 3.2.2：
Hierarchical Reinforcement Learning / 3.2.3：
Motivated Reinforcement Learning / 3.3：
Using a Motivation Signal in Addition to a Reward Signal / 3.3.1：
Using a Motivation Signal Instead of a Reward Signal / 3.3.2：
Comparing the Behaviour of Learning Agents / 3.4：
Player Satisfaction / 4.1：
Psychological Flow / 4.1.1：
Structural Flow / 4.1.2：
Formalising Non-Player Character Behaviour / 4.2：
Models of Optimality for Reinforcement Learning / 4.2.1：
Characteristics of Motivated Reinforcement Learning / 4.2.2：
Comparing Motivated Reinforcement Learning Agents / 4.3：
Statistical Model for Identifying Learned Tasks / 4.3.1：
Behavioural Variety / 4.3.2：
Behavioural Complexity / 4.3.3：
Developing Curious Characters Using Motivated Reinforcement Learning / 4.4：
Curiosity, Motivation and Attention Focus / 5：
Agents in Complex, Dynamic Environments / 5.1：
States / 5.1.1：
Actions / 5.1.2：
Reward and Motivation / 5.1.3：
Motivation and Attention Focus / 5.2：
Observations / 5.2.1：
Events / 5.2.2：
Tasks and Task Selection / 5.2.3：
Experience-Based Reward as Cognitive Motivation / 5.2.4：
Arbitration Functions / 5.2.5：
A General Experience-Based Motivation Function / 5.2.6：
Curiosity as Motivation for Support Characters / 5.3：
Curiosity as Interesting Events / 5.3.1：
Curiosity as Interesting and Competence / 5.3.2：
Motivated Reinforcement Learning Agents / 5.4：
A General Motivated Reinforcement Learning Model / 6.1：
Algorithms for Motivated Reinforcement Learning / 6.2：
Motivated Flat Reinforcement Learning / 6.2.1：
Motivated Multioption Reinforcement Learning / 6.2.2：
Motivated Hierarchical Reinforcement Learning / 6.2.3：
Curious Characters in Games / 6.3：
Curious Characters for Multiuser Games / 7：
Motivated Reinforcement Learning for Support Characters in Massively Multiplayer Online Role-Playing Games / 7.1：
Character Behaviour in Small-Scale, Isolated Games Locations / 7.2：
Case Studies of Individual Characters / 7.2.1：
General Trends in Character Behaviour / 7.2.2：
Curious Characters for Games in Complex, Dynamic Environments / 7.3：
Designing Characters That Can Multitask / 8.1：
Designing Characters for Complex Tasks / 8.1.1：
Games That Change While Characters Are Learning / 8.2.1：
Curious Characters for Games in Second Life / 8.3.1：
Motivated Reinforcement Learning in Open-Ended Simulation Games / 9.1：
Game Design / 9.1.1：
Character Design / 9.1.2：
Evaluating Character Behaviour in Response to Game Play Sequences / 9.2：
Discussion / 9.2.1：
Future / 9.3：
Towards the Future / 10：
Using Motivated Reinforcement Learning in Non-Player Characters / 10.1：
Other Gaming Applications for Motivated Reinforcement Learning / 10.2：
Dynamic Difficulty Adjustment / 10.2.1：
Procedural Content Generation / 10.2.2：
Beyond Curiosity / 10.3：
Biological Models of Motivation / 10.3.1：
Cognitive Models of Motivation / 10.3.2：
Social Models of Motivation / 10.3.3：
Combined Models of Motivation / 10.3.4：
New Models of Motivated Learning / 10.4：
Motivated Supervised Learning / 10.4.1：
Motivated Unsupervised Learning / 10.4.2：
Evaluating the Behaviour of Motivated Learning Agents / 10.5：
Concluding Remarks / 10.6：
Index / 10.7：
Non-Player Characters and Reinforcement Learning / Part I：
Non-Player Characters in Multiuser Games / 1：
Types of Multiuser Games / 1.1：
4.

電子ブック
ＥＢ
4. Motivated Reinforcement Learning
Kathryn E. Merrick, Mary Lou Maher
出版情報: SpringerLink Books - AutoHoldings , Springer Berlin Heidelberg, 2009
所蔵情報: loading…
目次情報: 続きを見る
Non-Player Characters and Reinforcement Learning / Part I：
Non-Player Characters in Multiuser Games / 1：
Types of Multiuser Games / 1.1：
Massively Multiplayer Online Role-Playing Games / 1.1.1：
Multiuser Simulation Games / 1.1.2：
Open-Ended Virtual Worlds / 1.1.3：
Character Roles in Multiuser Games / 1.2：
Existing Artificial Intelligence Techniques for Non-Player Characters in Multiuser Games / 1.3：
Reflexive Agents / 1.3.1：
Learning Agents / 1.3.2：
Evolutionary Agents / 1.3.3：
Smart Terrain / 1.3.4：
Summary / 1.4：
References / 1.5：
Motivation in Natural and Artificial Agents / 2：
Defining Motivation / 2.1：
Biological Theories of Motivation / 2.2：
Drive Theory / 2.2.1：
Motivational State Theory / 2.2.2：
Arousal / 2.2.3：
Cognitive Theories of Motivation / 2.3：
Curiosity / 2.3.1：
Operant Theory / 2.3.2：
Incentive / 2.3.3：
Achievement Motivation / 2.3.4：
Attribution Theory / 2.3.5：
Intrinsic Motivation / 2.3.6：
Social Theories of Motivation / 2.4：
Conformity / 2.4.1：
Cultural Effect / 2.4.2：
Evolution / 2.4.3：
Combined Motivation Theories / 2.5：
Maslow's Hierarchy of Needs / 2.5.1：
Existence Relatedness Growth Theory / 2.5.2：
Towards Motivated Reinforcement Learning / 2.6：
Defining Reinforcement Learning / 3.1：
Dynamic Programming / 3.1.1：
Monte Carlo Methods / 3.1.2：
Temporal Difference Learning / 3.1.3：
Reinforcement Learning in Complex Environments / 3.2：
Partially Observable Environments / 3.2.1：
Function Approximation / 3.2.2：
Hierarchical Reinforcement Learning / 3.2.3：
Motivated Reinforcement Learning / 3.3：
Using a Motivation Signal in Addition to a Reward Signal / 3.3.1：
Using a Motivation Signal Instead of a Reward Signal / 3.3.2：
Comparing the Behaviour of Learning Agents / 3.4：
Player Satisfaction / 4.1：
Psychological Flow / 4.1.1：
Structural Flow / 4.1.2：
Formalising Non-Player Character Behaviour / 4.2：
Models of Optimality for Reinforcement Learning / 4.2.1：
Characteristics of Motivated Reinforcement Learning / 4.2.2：
Comparing Motivated Reinforcement Learning Agents / 4.3：
Statistical Model for Identifying Learned Tasks / 4.3.1：
Behavioural Variety / 4.3.2：
Behavioural Complexity / 4.3.3：
Developing Curious Characters Using Motivated Reinforcement Learning / 4.4：
Curiosity, Motivation and Attention Focus / 5：
Agents in Complex, Dynamic Environments / 5.1：
States / 5.1.1：
Actions / 5.1.2：
Reward and Motivation / 5.1.3：
Motivation and Attention Focus / 5.2：
Observations / 5.2.1：
Events / 5.2.2：
Tasks and Task Selection / 5.2.3：
Experience-Based Reward as Cognitive Motivation / 5.2.4：
Arbitration Functions / 5.2.5：
A General Experience-Based Motivation Function / 5.2.6：
Curiosity as Motivation for Support Characters / 5.3：
Curiosity as Interesting Events / 5.3.1：
Curiosity as Interesting and Competence / 5.3.2：
Motivated Reinforcement Learning Agents / 5.4：
A General Motivated Reinforcement Learning Model / 6.1：
Algorithms for Motivated Reinforcement Learning / 6.2：
Motivated Flat Reinforcement Learning / 6.2.1：
Motivated Multioption Reinforcement Learning / 6.2.2：
Motivated Hierarchical Reinforcement Learning / 6.2.3：
Curious Characters in Games / 6.3：
Curious Characters for Multiuser Games / 7：
Motivated Reinforcement Learning for Support Characters in Massively Multiplayer Online Role-Playing Games / 7.1：
Character Behaviour in Small-Scale, Isolated Games Locations / 7.2：
Case Studies of Individual Characters / 7.2.1：
General Trends in Character Behaviour / 7.2.2：
Curious Characters for Games in Complex, Dynamic Environments / 7.3：
Designing Characters That Can Multitask / 8.1：
Designing Characters for Complex Tasks / 8.1.1：
Games That Change While Characters Are Learning / 8.2.1：
Curious Characters for Games in Second Life / 8.3.1：
Motivated Reinforcement Learning in Open-Ended Simulation Games / 9.1：
Game Design / 9.1.1：
Character Design / 9.1.2：
Evaluating Character Behaviour in Response to Game Play Sequences / 9.2：
Discussion / 9.2.1：
Future / 9.3：
Towards the Future / 10：
Using Motivated Reinforcement Learning in Non-Player Characters / 10.1：
Other Gaming Applications for Motivated Reinforcement Learning / 10.2：
Dynamic Difficulty Adjustment / 10.2.1：
Procedural Content Generation / 10.2.2：
Beyond Curiosity / 10.3：
Biological Models of Motivation / 10.3.1：
Cognitive Models of Motivation / 10.3.2：
Social Models of Motivation / 10.3.3：
Combined Models of Motivation / 10.3.4：
New Models of Motivated Learning / 10.4：
Motivated Supervised Learning / 10.4.1：
Motivated Unsupervised Learning / 10.4.2：
Evaluating the Behaviour of Motivated Learning Agents / 10.5：
Concluding Remarks / 10.6：
Index / 10.7：
Non-Player Characters and Reinforcement Learning / Part I：
Non-Player Characters in Multiuser Games / 1：
Types of Multiuser Games / 1.1：
5.

電子ブック
ＥＢ
5. On Probabilistic Conditional Independence Structures
Milan Studen?, Michael Jordan, Frank P. Kelly, Jon Kleinberg, Bernhard Sch?lkopf, Ian Witten
出版情報: Springer eBooks Computer Science , Springer London, 2005
所蔵情報: loading…
目次情報: 続きを見る
Introduction / 1：
Motivational thoughts / 1.1：
Goals of the monograph / 1.2：
Structure of the book / 1.3：
Basic Concepts / 2：
Conditional independence / 2.1：
Semi-graphoid properties / 2.2：
Formal independence models / 2.2.1：
Semi-graphoids / 2.2.2：
Elementary independence statements / 2.2.3：
Problem of axiomatic characterization / 2.2.4：
Classes of probability measures / 2.3：
Marginally continuous measures / 2.3.1：
Factorizable measures / 2.3.2：
Multiinformation and conditional product / 2.3.3：
Properties of multiinformation function / 2.3.4：
Positive measures / 2.3.5：
Gaussian measures / 2.3.6：
Basic construction / 2.3.7：
Imsets / 2.4：
Graphical Methods / 3：
Undirected graphs / 3.1：
Acyclic directed graphs / 3.2：
Classic chain graphs / 3.3：
Within classic graphical models / 3.4：
Decomposable models / 3.4.1：
Recursive causal graphs / 3.4.2：
Lattice conditional independence models / 3.4.3：
Bubble graphs / 3.4.4：
Advanced graphical models / 3.5：
General directed graphs / 3.5.1：
Reciprocal graphs / 3.5.2：
Joint-response chain graphs / 3.5.3：
Covariance graphs / 3.5.4：
Alternative chain graphs / 3.5.5：
Annotated graphs / 3.5.6：
Hidden variables / 3.5.7：
Ancestral graphs / 3.5.8：
MC graphs / 3.5.9：
Incompleteness of graphical approaches / 3.6：
Structural Imsets: Fundamentals / 4：
Basic class of distributions / 4.1：
Discrete measures / 4.1.1：
Regular Gaussian measures / 4.1.2：
Conditional Gaussian measures / 4.1.3：
Classes of structural imsets / 4.2：
Elementary imsets / 4.2.1：
Semi-elementary and combinatorial imsets / 4.2.2：
Structural imsets / 4.2.3：
Product formula induced by a structural imset / 4.3：
Examples of reference systems of measures / 4.3.1：
Topological assumptions / 4.3.2：
Markov condition / 4.4：
Semi-graphoid induced by a structural imset / 4.4.1：
Markovian measures / 4.4.2：
Equivalence result / 4.5：
Description of Probabilistic Models / 5：
Supermodular set functions / 5.1：
Semi-graphoid produced by a supermodular function / 5.1.1：
Quantitative equivalence of supermodular functions / 5.1.2：
Skeletal supermodular functions / 5.2：
Skeleton / 5.2.1：
Significance of skeletal imsets / 5.2.2：
Description of models by structural imsets / 5.3：
Galois connection / 5.4：
Formal concept analysis / 5.4.1：
Lattice of structural models / 5.4.2：
Equivalence and Implication / 6：
Two concepts of equivalence / 6.1：
Independence and Markov equivalence / 6.1.1：
Independence implication / 6.2：
Direct characterization of independence implication / 6.2.1：
Skeletal characterization of independence implication / 6.2.2：
Testing independence implication / 6.3：
Testing structural imsets / 6.3.1：
Grade / 6.3.2：
Invariants of independence equivalence / 6.4：
Adaptation to a distribution framework / 6.5：
The Problem of Representative Choice / 7：
Baricentral imsets / 7.1：
Standard imsets / 7.2：
Translation of DAG models / 7.2.1：
Translation of decomposable models / 7.2.2：
Imsets of the smallest degree / 7.3：
Decomposition implication / 7.3.1：
Minimal generators / 7.3.2：
Span / 7.4：
Determining and unimarginal classes / 7.4.1：
Imsets with the least lower class / 7.4.2：
Exclusivity of standard imsets / 7.4.3：
Dual description / 7.5：
Coportraits / 7.5.1：
Dual baricentral imsets and global view / 7.5.2：
Learning / 8：
Two approaches to learning / 8.1：
Quality criteria / 8.2：
Criteria for learning DAG models / 8.2.1：
Score equivalent criteria / 8.2.2：
Decomposable criteria / 8.2.3：
Regular criteria / 8.2.4：
Inclusion neighborhood / 8.3：
Standard imsets and learning / 8.4：
Inclusion neighborhood characterization / 8.4.1：
Regular criteria and standard imsets / 8.4.2：
Open Problems / 9：
Theoretical problems / 9.1：
Miscellaneous topics / 9.1.1：
Classification of skeletal imsets / 9.1.2：
Operations with structural models / 9.2：
Reductive operations / 9.2.1：
Expansive operations / 9.2.2：
Cumulative operations / 9.2.3：
Decomposition of structural models / 9.2.4：
Implementation tasks / 9.3：
Interpretation and learning tasks / 9.4：
Meaningful description of structural models / 9.4.1：
Tasks concerning distribution frameworks / 9.4.2：
Learning tasks / 9.4.3：
Appendix / A：
Classes of sets / A.1：
Posets and lattices / A.2：
Graphs / A.3：
Topological concepts / A.4：
Finite-dimensional subspaces and convex cones / A.5：
Linear subspaces / A.5.1：
Convex sets and cones / A.5.2：
Measure-theoretical concepts / A.6：
Measure and integral / A.6.1：
Basic measure-theoretical results / A.6.2：
Information-theoretical concepts / A.6.3：
Conditional probability / A.6.4：
Conditional independence in terms of ?-algebras / A.7：
Concepts from multivariate analysis / A.8：
Matrices / A.8.1：
Statistical characteristics of probability measures / A.8.2：
Multivariate Gaussian distributions / A.8.3：
Elementary statistical concepts / A.9：
Empirical concepts / A.9.1：
Statistical conception / A.9.2：
Likelihood function / A.9.3：
Testing statistical hypotheses / A.9.4：
Distribution framework / A.9.5：
List of Notation
List of Lemmas, Propositions etc
References
Index
Introduction / 1：
Motivational thoughts / 1.1：
Goals of the monograph / 1.2：
6.

電子ブック
ＥＢ
6. On Probabilistic Conditional Independence Structures
Milan Studený, Michael Jordan, Frank P. Kelly, Jon Kleinberg, Bernhard Schölkopf, Ian Witten
出版情報: SpringerLink Books - AutoHoldings , Springer London, 2005
所蔵情報: loading…
目次情報: 続きを見る
Introduction / 1：
Motivational thoughts / 1.1：
Goals of the monograph / 1.2：
Structure of the book / 1.3：
Basic Concepts / 2：
Conditional independence / 2.1：
Semi-graphoid properties / 2.2：
Formal independence models / 2.2.1：
Semi-graphoids / 2.2.2：
Elementary independence statements / 2.2.3：
Problem of axiomatic characterization / 2.2.4：
Classes of probability measures / 2.3：
Marginally continuous measures / 2.3.1：
Factorizable measures / 2.3.2：
Multiinformation and conditional product / 2.3.3：
Properties of multiinformation function / 2.3.4：
Positive measures / 2.3.5：
Gaussian measures / 2.3.6：
Basic construction / 2.3.7：
Imsets / 2.4：
Graphical Methods / 3：
Undirected graphs / 3.1：
Acyclic directed graphs / 3.2：
Classic chain graphs / 3.3：
Within classic graphical models / 3.4：
Decomposable models / 3.4.1：
Recursive causal graphs / 3.4.2：
Lattice conditional independence models / 3.4.3：
Bubble graphs / 3.4.4：
Advanced graphical models / 3.5：
General directed graphs / 3.5.1：
Reciprocal graphs / 3.5.2：
Joint-response chain graphs / 3.5.3：
Covariance graphs / 3.5.4：
Alternative chain graphs / 3.5.5：
Annotated graphs / 3.5.6：
Hidden variables / 3.5.7：
Ancestral graphs / 3.5.8：
MC graphs / 3.5.9：
Incompleteness of graphical approaches / 3.6：
Structural Imsets: Fundamentals / 4：
Basic class of distributions / 4.1：
Discrete measures / 4.1.1：
Regular Gaussian measures / 4.1.2：
Conditional Gaussian measures / 4.1.3：
Classes of structural imsets / 4.2：
Elementary imsets / 4.2.1：
Semi-elementary and combinatorial imsets / 4.2.2：
Structural imsets / 4.2.3：
Product formula induced by a structural imset / 4.3：
Examples of reference systems of measures / 4.3.1：
Topological assumptions / 4.3.2：
Markov condition / 4.4：
Semi-graphoid induced by a structural imset / 4.4.1：
Markovian measures / 4.4.2：
Equivalence result / 4.5：
Description of Probabilistic Models / 5：
Supermodular set functions / 5.1：
Semi-graphoid produced by a supermodular function / 5.1.1：
Quantitative equivalence of supermodular functions / 5.1.2：
Skeletal supermodular functions / 5.2：
Skeleton / 5.2.1：
Significance of skeletal imsets / 5.2.2：
Description of models by structural imsets / 5.3：
Galois connection / 5.4：
Formal concept analysis / 5.4.1：
Lattice of structural models / 5.4.2：
Equivalence and Implication / 6：
Two concepts of equivalence / 6.1：
Independence and Markov equivalence / 6.1.1：
Independence implication / 6.2：
Direct characterization of independence implication / 6.2.1：
Skeletal characterization of independence implication / 6.2.2：
Testing independence implication / 6.3：
Testing structural imsets / 6.3.1：
Grade / 6.3.2：
Invariants of independence equivalence / 6.4：
Adaptation to a distribution framework / 6.5：
The Problem of Representative Choice / 7：
Baricentral imsets / 7.1：
Standard imsets / 7.2：
Translation of DAG models / 7.2.1：
Translation of decomposable models / 7.2.2：
Imsets of the smallest degree / 7.3：
Decomposition implication / 7.3.1：
Minimal generators / 7.3.2：
Span / 7.4：
Determining and unimarginal classes / 7.4.1：
Imsets with the least lower class / 7.4.2：
Exclusivity of standard imsets / 7.4.3：
Dual description / 7.5：
Coportraits / 7.5.1：
Dual baricentral imsets and global view / 7.5.2：
Learning / 8：
Two approaches to learning / 8.1：
Quality criteria / 8.2：
Criteria for learning DAG models / 8.2.1：
Score equivalent criteria / 8.2.2：
Decomposable criteria / 8.2.3：
Regular criteria / 8.2.4：
Inclusion neighborhood / 8.3：
Standard imsets and learning / 8.4：
Inclusion neighborhood characterization / 8.4.1：
Regular criteria and standard imsets / 8.4.2：
Open Problems / 9：
Theoretical problems / 9.1：
Miscellaneous topics / 9.1.1：
Classification of skeletal imsets / 9.1.2：
Operations with structural models / 9.2：
Reductive operations / 9.2.1：
Expansive operations / 9.2.2：
Cumulative operations / 9.2.3：
Decomposition of structural models / 9.2.4：
Implementation tasks / 9.3：
Interpretation and learning tasks / 9.4：
Meaningful description of structural models / 9.4.1：
Tasks concerning distribution frameworks / 9.4.2：
Learning tasks / 9.4.3：
Appendix / A：
Classes of sets / A.1：
Posets and lattices / A.2：
Graphs / A.3：
Topological concepts / A.4：
Finite-dimensional subspaces and convex cones / A.5：
Linear subspaces / A.5.1：
Convex sets and cones / A.5.2：
Measure-theoretical concepts / A.6：
Measure and integral / A.6.1：
Basic measure-theoretical results / A.6.2：
Information-theoretical concepts / A.6.3：
Conditional probability / A.6.4：
Conditional independence in terms of ?-algebras / A.7：
Concepts from multivariate analysis / A.8：
Matrices / A.8.1：
Statistical characteristics of probability measures / A.8.2：
Multivariate Gaussian distributions / A.8.3：
Elementary statistical concepts / A.9：
Empirical concepts / A.9.1：
Statistical conception / A.9.2：
Likelihood function / A.9.3：
Testing statistical hypotheses / A.9.4：
Distribution framework / A.9.5：
List of Notation
List of Lemmas, Propositions etc
References
Index
Introduction / 1：
Motivational thoughts / 1.1：
Goals of the monograph / 1.2：
7.

電子ブック
ＥＢ
7. Pedagogically Founded Courseware Generation for Web-Based Learning
Carsten Ullrich, J?rg Siekmann
出版情報: Springer eBooks Computer Science , Springer Berlin Heidelberg, 2008
所蔵情報: loading…
目次情報: 続きを見る
Preliminaries / Part I：
Introduction / 1：
Motivation / 1.1：
Contributions / 1.2：
Service-Oriented Course Generation / 1.2.1：
Modeling of Pedagogical Knowledge / 1.2.2：
Adaptivity in Generated Courses / 1.2.3：
Evaluation / 1.2.4：
Overview / 1.3：
Relevant Technologies / 2：
Basic Terminology / 2.1：
Semantic Web Technologies / 2.2：
Extensible Markup Language / 2.2.1：
Resource Description Framework / 2.2.2：
OWL Web Ontology Language / 2.2.3：
E-learning Standards / 2.3：
Learning Object Metadata / 2.3.1：
IMS Content Packaging / 2.3.2：
IMS Simple Sequencing / 2.3.3：
IMS Learning Design / 2.3.4：
Mathematics in the Web / 2.4：
OMDoc (Open Mathematical Documents) / 2.4.1：
The Learning Environment ActiveMath / 2.4.2：
Course Generation / 2.5：
Hierarchical Task Network Planning / 2.6：
Introduction to AI-Planning / 2.6.1：
Introduction to Hierarchical Task Network Planning / 2.6.2：
SHOP2 and JSHOP2 / 2.6.3：
JSHOP2 Formalism / 2.6.4：
Descriptive and Prescriptive Learning Theories / 3：
Behaviorism / 3.1：
Cognitivism / 3.2：
Constructivism / 3.3：
Instructional Design / 3.4：
Competency-Based Learning / 3.5：
Mathematical Competencies / 3.5.1：
Competency Levels / 3.5.2：
PAIGOS / Part II：
General Principles / 4：
An Ontology of Instructional Objects / 4.1：
Description of the Ontology / 4.1.1：
Why an Ontology? / 4.1.3：
Applications of the Ontology / 4.1.4：
A Mediator for Accessing Learning Object Repositories / 4.2：
Related Work / 4.2.1：
Overview of the Mediator Architecture / 4.2.2：
Querying the Mediator / 4.2.3：
Ontology Mapping and Query Rewriting / 4.2.4：
Repository Interface and Caching / 4.2.5：
Limitations of the Mediator as an Educational Service / 4.2.6：
Pedagogical Tasks, Methods and Strategies / 4.3：
Representing Course Generation Knowledge in an HTN Planner / 4.4：
Mapping Pedagogical Tasks onto HTN Tasks / 4.4.1：
Course Generation Planning Problems / 4.4.3：
Critical and Optional Tasks / 4.4.4：
Basic General Purpose Axioms and Operators / 4.5：
Testing for Equality / 4.5.1：
List Manipulation / 4.5.2：
Binding a Variable to All Terms of a Term List / 4.5.3：
Manipulating the World State / 4.5.4：
Basic Operators and Methods of the Course Generation Domain / 4.6：
Inserting References to Educational Resources / 4.6.1：
Starting and Ending Sections / 4.6.2：
Inserting References to Learning-Support Services / 4.6.3：
An Operator for Dynamic Text Generation / 4.6.4：
Dynamic Subtask Expansion / 4.6.5：
Accessing Information about Educational Resources / 4.6.6：
Axioms for Accessing the Learner Model / 4.6.7：
Processing Resources Depending on Learner Characteristics / 4.6.8：
Initializing and Manipulating Information about the Learning Goal / 4.6.9：
Converting a Plan into a Course / 4.7：
Generating Structure and Adaptivity: Dynamic Tasks / 4.8：
Generation of Narrative Bridges and Structure / 4.9：
Empirical Findings / 4.9.1：
Operator and Methods for Text Generation / 4.9.2：
Symbolic Representations of Dynamic Text Items / 4.9.3：
Generation of Structure Information / 4.9.4：
Summary / 4.10：
Course Generation in Practice: Formalized Scenarios / 5：
Moderate Constructivist Competency-Based Scenarios / 5.1：
Course Generation and Constructivism - a Contradiction? / 5.1.1：
Selecting Exercises / 5.1.2：
Selecting Examples / 5.1.3：
Scenario "Discover" / 5.1.4：
Scenario "Rehearse" / 5.1.5：
Scenario "Connect" / 5.1.6：
Scenario "Train Intensively" / 5.1.7：
Scenario "Train Competencies" / 5.1.8：
Scenario "Exam Simulation" / 5.1.9：
Course Generation Based on Instructional Design Principles / 5.2：
Merrill's "First Principles of Instruction" / 5.2.1：
Scenario "Guided Tour" / 5.2.2：
Implementation and Integration / 6：
Implementation / 6.1：
Integration of PAIGOS in ActiveMath / 6.2：
Course Generation in ActiveMath / 6.2.1：
Dynamically Generated Elements in a Table of Contents / 6.2.2：
Usage of Learning-Support Services in ActiveMath / 6.2.3：
Template-Based Generation of Narrative Bridges / 6.2.4：
PAIGOS as a Service in ActiveMath / 6.2.5：
Course Generation as a Web-Service / 6.3：
Interfaces / 6.3.1：
Technical Evaluations and Use Cases / 7：
Evaluation of the Ontology / 7.1.1：
Mediator Use Cases and Evaluations / 7.1.2：
Course Generation Use Cases and Evaluations / 7.1.3：
Performance of PAIGOS / 7.1.4：
Discussion / 7.1.5：
Formative and Summative Evaluation / 7.2：
Formative Evaluations / 7.2.1：
Summative Evaluation / 7.2.2：
Conclusions / 7.2.3：
Early Work / 8：
Generic Tutoring Environment / 8.2：
Dynamic Courseware Generator / 8.3：
ACE/WINDS / 8.4：
Former Course Generator of ActiveMath / 8.5：
APeLS/iClass / 8.6：
SeLeNe / 8.7：
Statistical Methods for Course Generation / 8.8：
Approaches Using Hierarchical Task Network Planning / 8.9：
Ontologies for Instructional Design / 8.10：
Future Work and Acknowledgments / 9：
Future Work / 9.1：
Complete List of User Comments
References
Index
Preliminaries / Part I：
Introduction / 1：
Motivation / 1.1：
8.

電子ブック
ＥＢ
8. Pedagogically Founded Courseware Generation for Web-Based Learning
Carsten Ullrich, Jörg Siekmann
出版情報: SpringerLink Books - AutoHoldings , Springer Berlin Heidelberg, 2008
所蔵情報: loading…
目次情報: 続きを見る
Preliminaries / Part I：
Introduction / 1：
Motivation / 1.1：
Contributions / 1.2：
Service-Oriented Course Generation / 1.2.1：
Modeling of Pedagogical Knowledge / 1.2.2：
Adaptivity in Generated Courses / 1.2.3：
Evaluation / 1.2.4：
Overview / 1.3：
Relevant Technologies / 2：
Basic Terminology / 2.1：
Semantic Web Technologies / 2.2：
Extensible Markup Language / 2.2.1：
Resource Description Framework / 2.2.2：
OWL Web Ontology Language / 2.2.3：
E-learning Standards / 2.3：
Learning Object Metadata / 2.3.1：
IMS Content Packaging / 2.3.2：
IMS Simple Sequencing / 2.3.3：
IMS Learning Design / 2.3.4：
Mathematics in the Web / 2.4：
OMDoc (Open Mathematical Documents) / 2.4.1：
The Learning Environment ActiveMath / 2.4.2：
Course Generation / 2.5：
Hierarchical Task Network Planning / 2.6：
Introduction to AI-Planning / 2.6.1：
Introduction to Hierarchical Task Network Planning / 2.6.2：
SHOP2 and JSHOP2 / 2.6.3：
JSHOP2 Formalism / 2.6.4：
Descriptive and Prescriptive Learning Theories / 3：
Behaviorism / 3.1：
Cognitivism / 3.2：
Constructivism / 3.3：
Instructional Design / 3.4：
Competency-Based Learning / 3.5：
Mathematical Competencies / 3.5.1：
Competency Levels / 3.5.2：
PAIGOS / Part II：
General Principles / 4：
An Ontology of Instructional Objects / 4.1：
Description of the Ontology / 4.1.1：
Why an Ontology? / 4.1.3：
Applications of the Ontology / 4.1.4：
A Mediator for Accessing Learning Object Repositories / 4.2：
Related Work / 4.2.1：
Overview of the Mediator Architecture / 4.2.2：
Querying the Mediator / 4.2.3：
Ontology Mapping and Query Rewriting / 4.2.4：
Repository Interface and Caching / 4.2.5：
Limitations of the Mediator as an Educational Service / 4.2.6：
Pedagogical Tasks, Methods and Strategies / 4.3：
Representing Course Generation Knowledge in an HTN Planner / 4.4：
Mapping Pedagogical Tasks onto HTN Tasks / 4.4.1：
Course Generation Planning Problems / 4.4.3：
Critical and Optional Tasks / 4.4.4：
Basic General Purpose Axioms and Operators / 4.5：
Testing for Equality / 4.5.1：
List Manipulation / 4.5.2：
Binding a Variable to All Terms of a Term List / 4.5.3：
Manipulating the World State / 4.5.4：
Basic Operators and Methods of the Course Generation Domain / 4.6：
Inserting References to Educational Resources / 4.6.1：
Starting and Ending Sections / 4.6.2：
Inserting References to Learning-Support Services / 4.6.3：
An Operator for Dynamic Text Generation / 4.6.4：
Dynamic Subtask Expansion / 4.6.5：
Accessing Information about Educational Resources / 4.6.6：
Axioms for Accessing the Learner Model / 4.6.7：
Processing Resources Depending on Learner Characteristics / 4.6.8：
Initializing and Manipulating Information about the Learning Goal / 4.6.9：
Converting a Plan into a Course / 4.7：
Generating Structure and Adaptivity: Dynamic Tasks / 4.8：
Generation of Narrative Bridges and Structure / 4.9：
Empirical Findings / 4.9.1：
Operator and Methods for Text Generation / 4.9.2：
Symbolic Representations of Dynamic Text Items / 4.9.3：
Generation of Structure Information / 4.9.4：
Summary / 4.10：
Course Generation in Practice: Formalized Scenarios / 5：
Moderate Constructivist Competency-Based Scenarios / 5.1：
Course Generation and Constructivism - a Contradiction? / 5.1.1：
Selecting Exercises / 5.1.2：
Selecting Examples / 5.1.3：
Scenario "Discover" / 5.1.4：
Scenario "Rehearse" / 5.1.5：
Scenario "Connect" / 5.1.6：
Scenario "Train Intensively" / 5.1.7：
Scenario "Train Competencies" / 5.1.8：
Scenario "Exam Simulation" / 5.1.9：
Course Generation Based on Instructional Design Principles / 5.2：
Merrill's "First Principles of Instruction" / 5.2.1：
Scenario "Guided Tour" / 5.2.2：
Implementation and Integration / 6：
Implementation / 6.1：
Integration of PAIGOS in ActiveMath / 6.2：
Course Generation in ActiveMath / 6.2.1：
Dynamically Generated Elements in a Table of Contents / 6.2.2：
Usage of Learning-Support Services in ActiveMath / 6.2.3：
Template-Based Generation of Narrative Bridges / 6.2.4：
PAIGOS as a Service in ActiveMath / 6.2.5：
Course Generation as a Web-Service / 6.3：
Interfaces / 6.3.1：
Technical Evaluations and Use Cases / 7：
Evaluation of the Ontology / 7.1.1：
Mediator Use Cases and Evaluations / 7.1.2：
Course Generation Use Cases and Evaluations / 7.1.3：
Performance of PAIGOS / 7.1.4：
Discussion / 7.1.5：
Formative and Summative Evaluation / 7.2：
Formative Evaluations / 7.2.1：
Summative Evaluation / 7.2.2：
Conclusions / 7.2.3：
Early Work / 8：
Generic Tutoring Environment / 8.2：
Dynamic Courseware Generator / 8.3：
ACE/WINDS / 8.4：
Former Course Generator of ActiveMath / 8.5：
APeLS/iClass / 8.6：
SeLeNe / 8.7：
Statistical Methods for Course Generation / 8.8：
Approaches Using Hierarchical Task Network Planning / 8.9：
Ontologies for Instructional Design / 8.10：
Future Work and Acknowledgments / 9：
Future Work / 9.1：
Complete List of User Comments
References
Index
Preliminaries / Part I：
Introduction / 1：
Motivation / 1.1：
9.

電子ブック
ＥＢ
9. Machine Learning Techniques for Multimedia
D. M. Gabbay, Matthieu Cord, J. Siekmann
出版情報: Springer eBooks Computer Science , Springer Berlin Heidelberg, 2008
所蔵情報: loading…
目次情報: 続きを見る
Introduction to Learning Principles for Multimedia Data / Part I：
Introduction to Bayesian Methods and Decision Theory / Simon P. Wilson ; Rozenn Dahyot ; Padraig Cunningham1：
Introduction / 1.1：
Uncertainty and Probability / 1.2：
Quantifying Uncertainty / 1.2.1：
The Laws of Probability / 1.2.2：
Interpreting Probability / 1.2.3：
The Partition Law and Bayes' Law / 1.2.4：
Probability Models, Parameters and Likelihoods / 1.3：
Bayesian Statistical Learning / 1.4：
Implementing Bayesian Statistical Learning Methods / 1.5：
Direct Simulation Methods / 1.5.1：
Markov Chain Monte Carlo / 1.5.2：
Monte Carlo Integration / 1.5.3：
Optimization Methods / 1.5.4：
Decision Theory / 1.6：
Utility and Choosing the Optimal Decision / 1.6.1：
Where Is the Utility? / 1.6.2：
Native Bayes / 1.7：
Further Reading / 1.8：
References
Supervised Learning / Matthieu Cord ; Sarah Jane Delany2：
Introduction to Statistical Learning / 2.1：
Risk Minimization / 2.2.1：
Empirical Risk Minimization / 2.2.2：
Risk Bounds / 2.2.3：
Support Vector Machines and Kernels / 2.3：
Linear Classification: SVM Principle / 2.3.1：
Soft Margin / 2.3.2：
Kernel-Based Classification / 2.3.3：
Nearest Neighbour Classification / 2.4：
Similarity and Distance Metrics / 2.4.1：
Other Distance Metrics for Multimedia Data / 2.4.2：
Computational Complexity / 2.4.3：
Instance Selection and Noise Reduction / 2.4.4：
k-NN: Advantages and Disadvantages / 2.4.5：
Ensemble Techniques / 2.5：
Bias-Variance Analysis of Error / 2.5.1：
Bagging / 2.5.3：
Random Forests / 2.5.4：
Boosting / 2.5.5：
Summary / 2.6：
Unsupervised Learning and Clustering / Derek Greene ; Páadraig Cunningham ; Rudolf Mayer3：
Basic Clustering Techniques / 3.1：
k-Means Clustering / 3.2.1：
Fuzzy Clustering / 3.2.2：
Hierarchical Clustering / 3.2.3：
Modern Clustering Techniques / 3.3：
Kernel Clustering / 3.3.1：
Spectral Clustering / 3.3.2：
Self-organizing Maps / 3.4：
SOM Architecture / 3.4.1：
SOM Algorithm / 3.4.2：
Self-organizing Map and Clustering / 3.4.3：
Variations of the Self-organizing Map / 3.4.4：
Cluster Validation / 3.5：
Internal Validation / 3.5.1：
External Validation / 3.5.2：
Stability-Based Techniques / 3.5.3：
Dimension Reduction / 3.6：
Feature Transformation / 4.1：
Principal Component Analysis / 4.2.1：
Linear Discriminant Analysis / 4.2.2：
Feature Selection / 4.3：
Feature Selection in Supervised Learning / 4.3.1：
Unsupervised Feature Selection / 4.3.2：
Conclusions / 4.4：
Multimedia Applications / Part II：
Online Content-Based Image Retrieval Using Active Learning / Philippe-Henri Gosselin5：
Database Representation: Features and Similarity / 5.1：
Visual Features / 5.2.1：
Signature Based on Visual Pattern Dictionary / 5.2.2：
Similarity / 5.2.3：
Kernel Framework / 5.2.4：
Experiments / 5.2.5：
Classification Framework for Image Collection / 5.3：
Classification Methods for CBIR / 5.3.1：
Query Updating Scheme / 5.3.2：
Active Learning for CBIR / 5.3.3：
Notations for Selective Sampling Optimization / 5.4.1：
Active Learning Methods / 5.4.2：
Further Insights on Active Learning for CBIR / 5.5：
Active Boundary Correction / 5.5.1：
MAP vs Classification Error / 5.5.2：
Batch Selection / 5.5.3：
CBIR Interface: Result Display and Interaction / 5.5.4：
Conservative Learning for Object Detectors / Peter M. Roth ; Horst Bischof6：
Online Conservative Learning / 6.1：
Motion Detection / 6.2.1：
Reconstructive Model / 6.2.2：
Online AdaBoost for Feature Selection / 6.2.3：
Conservative Update Rules / 6.2.4：
Experimental Results / 6.3：
Description of Experiments / 6.3.1：
CoffeeCam / 6.3.2：
Switch to Caviar / 6.3.3：
Further Detection Results / 6.3.4：
Summary and Conclusions / 6.4：
Machine Learning Techniques for Face Analysis / Roberto Valenti ; Nicu Sebe ; Theo Gevers ; Ira Cohen7：
Background / 7.1：
Face Detection / 7.2.1：
Facial Feature Detection / 7.2.2：
Emotion Recognition Research / 7.2.3：
Learning Classifiers for Human-Computer Interaction / 7.3：
Model Is Correct / 7.3.1：
Model Is Incorrect / 7.3.2：
Discussion / 7.3.3：
Learning the Structure of Bayesian Network Classifiers / 7.4：
Bayesian Networks / 7.4.1：
Switching Between Simple Models / 7.4.2：
Beyond Simple Models / 7.4.3：
Classification-Driven Stochastic Structure Search / 7.4.4：
Should Unlabeled Be Weighed Differently? / 7.4.5：
Active Learning / 7.4.6：
Face Detection Experiments / 7.4.7：
Facial Expression Recognition Experiments / 7.5.2：
Mental Search in Image Databases: Implicit Versus Explicit Content Query / Julien Fauqueur ; Nozha Boujemaa7.6：
"Mental Image Search" Versus Other Search Paradigms / 8.1：
Implicit Content Query: Mental Image Search Using Bayesian Inference / 8.3：
Bayesian Inference for CBIR / 8.3.1：
Mental Image Category Search / 8.3.2：
Evaluation / 8.3.3：
Remarks / 8.3.4：
Explicit Content Query: Mental Image Search by Visual Composition Formulation / 8.4：
System Summary / 8.4.1：
Visual Thesaurus Construction / 8.4.2：
Symbolic Indexing, Boolean Search and Range Query Mechanism / 8.4.3：
Results / 8.4.4：
Combining Textual and Visual Information for Semantic Labeling of Images and Videos / Pinar Duygulu ; Muhammet Başstan ; Derya Ozkan8.4.5：
Semantic Labeling of Images / 9.1：
Translation Approach / 9.3：
Learning Correspondences Between Words and Regions / 9.3.1：
Linking Visual Elements to Words in News Videos / 9.3.2：
Translation Approach to Solve Video Association Problem / 9.3.3：
Experiments on News Videos Data Set / 9.3.4：
Naming Faces in News / 9.4：
Integrating Names and Faces / 9.4.1：
Finding Similarity of Faces / 9.4.2：
Finding the Densest Component in the Similarity Graph / 9.4.3：
Conclusions and Discussion / 9.4.4：
Machine Learning for Semi-structured Multimedia Documents: Application to Pornographic Filtering and Thematic Categorization. / Ludovic Denoyer ; Patrick Gallinari10：
Previous Work / 10.1：
Structured Document Classification / 10.2.1：
Multimedia Documents / 10.2.2：
Multimedia Generative Model / 10.3：
Classification of Documents / 10.3.1：
Generative Model / 10.3.2：
Description / 10.3.3：
Learning the Meta Model / 10.4：
Maximization of Lstructure / 10.4.1：
Maximization of Lcontent / 10.4.2：
Local Generative Models for Text and Image / 10.5：
Modelling a Piece of Text with Naive Bayes / 10.5.1：
Image Model / 10.5.2：
Models and Evaluation / 10.6：
Corpora / 10.6.2：
Results over the Pornographic Corpus / 10.6.3：
Results over the Wikipedia Multimedia Categorization Corpus / 10.6.4：
Conclusion / 10.7：
Classification and Clustering of Music for Novel Music Access Applications / Thomas Lidy ; Andreas Rauber11：
Feature Extraction from Audio / 11.1：
Low-Level Audio Features / 11.2.1：
MPEG-7 Audio Descriptors / 11.2.2：
MFCCs / 11.2.3：
MARSYAS Features / 11.2.4：
Rhythm Patterns / 11.2.5：
Statistical Spectrum Descriptors / 11.2.6：
Rhythm Histograms / 11.2.7：
Automatic Classifications of Music into Genres / 11.3：
Evaluation Through Music Classification / 11.3.1：
Benchmark Data Sets for Music Classification / 11.3.2：
Creating and Visualizing Music Maps Based on Self-organizing Maps / 11.4：
Class Visualization / 11.4.1：
Hit Histograms / 11.4.2：
U-Matrix / 11.4.3：
P-Matrix / 11.4.4：
U*-matrix / 11.4.5：
Gradient Fields / 11.4.6：
Component Planes / 11.4.7：
Smoothed Data Histograms / 11.4.8：
PlaySOM - Interaction with Music Maps / 11.5：
Interface / 11.5.1：
Interaction / 11.5.2：
Playlist Creation / 11.5.3：
PocketSOMPlayer - Music Retrieval on Mobile Devices / 11.6：
Playing Scenarios / 11.6.1：
Index / 11.6.3：
Introduction to Learning Principles for Multimedia Data / Part I：
Introduction to Bayesian Methods and Decision Theory / Simon P. Wilson ; Rozenn Dahyot ; Padraig Cunningham1：
Introduction / 1.1：
10.

電子ブック
ＥＢ
10. Machine Learning Techniques for Multimedia
D. M. Gabbay, Matthieu Cord, J. Siekmann, Pádraig Cunningham
出版情報: SpringerLink Books - AutoHoldings , Springer Berlin Heidelberg, 2008
所蔵情報: loading…
目次情報: 続きを見る
Introduction to Learning Principles for Multimedia Data / Part I：
Introduction to Bayesian Methods and Decision Theory / Simon P. Wilson ; Rozenn Dahyot ; Padraig Cunningham1：
Introduction / 1.1：
Uncertainty and Probability / 1.2：
Quantifying Uncertainty / 1.2.1：
The Laws of Probability / 1.2.2：
Interpreting Probability / 1.2.3：
The Partition Law and Bayes' Law / 1.2.4：
Probability Models, Parameters and Likelihoods / 1.3：
Bayesian Statistical Learning / 1.4：
Implementing Bayesian Statistical Learning Methods / 1.5：
Direct Simulation Methods / 1.5.1：
Markov Chain Monte Carlo / 1.5.2：
Monte Carlo Integration / 1.5.3：
Optimization Methods / 1.5.4：
Decision Theory / 1.6：
Utility and Choosing the Optimal Decision / 1.6.1：
Where Is the Utility? / 1.6.2：
Native Bayes / 1.7：
Further Reading / 1.8：
References
Supervised Learning / Matthieu Cord ; Sarah Jane Delany2：
Introduction to Statistical Learning / 2.1：
Risk Minimization / 2.2.1：
Empirical Risk Minimization / 2.2.2：
Risk Bounds / 2.2.3：
Support Vector Machines and Kernels / 2.3：
Linear Classification: SVM Principle / 2.3.1：
Soft Margin / 2.3.2：
Kernel-Based Classification / 2.3.3：
Nearest Neighbour Classification / 2.4：
Similarity and Distance Metrics / 2.4.1：
Other Distance Metrics for Multimedia Data / 2.4.2：
Computational Complexity / 2.4.3：
Instance Selection and Noise Reduction / 2.4.4：
k-NN: Advantages and Disadvantages / 2.4.5：
Ensemble Techniques / 2.5：
Bias-Variance Analysis of Error / 2.5.1：
Bagging / 2.5.3：
Random Forests / 2.5.4：
Boosting / 2.5.5：
Summary / 2.6：
Unsupervised Learning and Clustering / Derek Greene ; Páadraig Cunningham ; Rudolf Mayer3：
Basic Clustering Techniques / 3.1：
k-Means Clustering / 3.2.1：
Fuzzy Clustering / 3.2.2：
Hierarchical Clustering / 3.2.3：
Modern Clustering Techniques / 3.3：
Kernel Clustering / 3.3.1：
Spectral Clustering / 3.3.2：
Self-organizing Maps / 3.4：
SOM Architecture / 3.4.1：
SOM Algorithm / 3.4.2：
Self-organizing Map and Clustering / 3.4.3：
Variations of the Self-organizing Map / 3.4.4：
Cluster Validation / 3.5：
Internal Validation / 3.5.1：
External Validation / 3.5.2：
Stability-Based Techniques / 3.5.3：
Dimension Reduction / 3.6：
Feature Transformation / 4.1：
Principal Component Analysis / 4.2.1：
Linear Discriminant Analysis / 4.2.2：
Feature Selection / 4.3：
Feature Selection in Supervised Learning / 4.3.1：
Unsupervised Feature Selection / 4.3.2：
Conclusions / 4.4：
Multimedia Applications / Part II：
Online Content-Based Image Retrieval Using Active Learning / Philippe-Henri Gosselin5：
Database Representation: Features and Similarity / 5.1：
Visual Features / 5.2.1：
Signature Based on Visual Pattern Dictionary / 5.2.2：
Similarity / 5.2.3：
Kernel Framework / 5.2.4：
Experiments / 5.2.5：
Classification Framework for Image Collection / 5.3：
Classification Methods for CBIR / 5.3.1：
Query Updating Scheme / 5.3.2：
Active Learning for CBIR / 5.3.3：
Notations for Selective Sampling Optimization / 5.4.1：
Active Learning Methods / 5.4.2：
Further Insights on Active Learning for CBIR / 5.5：
Active Boundary Correction / 5.5.1：
MAP vs Classification Error / 5.5.2：
Batch Selection / 5.5.3：
CBIR Interface: Result Display and Interaction / 5.5.4：
Conservative Learning for Object Detectors / Peter M. Roth ; Horst Bischof6：
Online Conservative Learning / 6.1：
Motion Detection / 6.2.1：
Reconstructive Model / 6.2.2：
Online AdaBoost for Feature Selection / 6.2.3：
Conservative Update Rules / 6.2.4：
Experimental Results / 6.3：
Description of Experiments / 6.3.1：
CoffeeCam / 6.3.2：
Switch to Caviar / 6.3.3：
Further Detection Results / 6.3.4：
Summary and Conclusions / 6.4：
Machine Learning Techniques for Face Analysis / Roberto Valenti ; Nicu Sebe ; Theo Gevers ; Ira Cohen7：
Background / 7.1：
Face Detection / 7.2.1：
Facial Feature Detection / 7.2.2：
Emotion Recognition Research / 7.2.3：
Learning Classifiers for Human-Computer Interaction / 7.3：
Model Is Correct / 7.3.1：
Model Is Incorrect / 7.3.2：
Discussion / 7.3.3：
Learning the Structure of Bayesian Network Classifiers / 7.4：
Bayesian Networks / 7.4.1：
Switching Between Simple Models / 7.4.2：
Beyond Simple Models / 7.4.3：
Classification-Driven Stochastic Structure Search / 7.4.4：
Should Unlabeled Be Weighed Differently? / 7.4.5：
Active Learning / 7.4.6：
Face Detection Experiments / 7.4.7：
Facial Expression Recognition Experiments / 7.5.2：
Mental Search in Image Databases: Implicit Versus Explicit Content Query / Julien Fauqueur ; Nozha Boujemaa7.6：
"Mental Image Search" Versus Other Search Paradigms / 8.1：
Implicit Content Query: Mental Image Search Using Bayesian Inference / 8.3：
Bayesian Inference for CBIR / 8.3.1：
Mental Image Category Search / 8.3.2：
Evaluation / 8.3.3：
Remarks / 8.3.4：
Explicit Content Query: Mental Image Search by Visual Composition Formulation / 8.4：
System Summary / 8.4.1：
Visual Thesaurus Construction / 8.4.2：
Symbolic Indexing, Boolean Search and Range Query Mechanism / 8.4.3：
Results / 8.4.4：
Combining Textual and Visual Information for Semantic Labeling of Images and Videos / Pinar Duygulu ; Muhammet Başstan ; Derya Ozkan8.4.5：
Semantic Labeling of Images / 9.1：
Translation Approach / 9.3：
Learning Correspondences Between Words and Regions / 9.3.1：
Linking Visual Elements to Words in News Videos / 9.3.2：
Translation Approach to Solve Video Association Problem / 9.3.3：
Experiments on News Videos Data Set / 9.3.4：
Naming Faces in News / 9.4：
Integrating Names and Faces / 9.4.1：
Finding Similarity of Faces / 9.4.2：
Finding the Densest Component in the Similarity Graph / 9.4.3：
Conclusions and Discussion / 9.4.4：
Machine Learning for Semi-structured Multimedia Documents: Application to Pornographic Filtering and Thematic Categorization. / Ludovic Denoyer ; Patrick Gallinari10：
Previous Work / 10.1：
Structured Document Classification / 10.2.1：
Multimedia Documents / 10.2.2：
Multimedia Generative Model / 10.3：
Classification of Documents / 10.3.1：
Generative Model / 10.3.2：
Description / 10.3.3：
Learning the Meta Model / 10.4：
Maximization of Lstructure / 10.4.1：
Maximization of Lcontent / 10.4.2：
Local Generative Models for Text and Image / 10.5：
Modelling a Piece of Text with Naive Bayes / 10.5.1：
Image Model / 10.5.2：
Models and Evaluation / 10.6：
Corpora / 10.6.2：
Results over the Pornographic Corpus / 10.6.3：
Results over the Wikipedia Multimedia Categorization Corpus / 10.6.4：
Conclusion / 10.7：
Classification and Clustering of Music for Novel Music Access Applications / Thomas Lidy ; Andreas Rauber11：
Feature Extraction from Audio / 11.1：
Low-Level Audio Features / 11.2.1：
MPEG-7 Audio Descriptors / 11.2.2：
MFCCs / 11.2.3：
MARSYAS Features / 11.2.4：
Rhythm Patterns / 11.2.5：
Statistical Spectrum Descriptors / 11.2.6：
Rhythm Histograms / 11.2.7：
Automatic Classifications of Music into Genres / 11.3：
Evaluation Through Music Classification / 11.3.1：
Benchmark Data Sets for Music Classification / 11.3.2：
Creating and Visualizing Music Maps Based on Self-organizing Maps / 11.4：
Class Visualization / 11.4.1：
Hit Histograms / 11.4.2：
U-Matrix / 11.4.3：
P-Matrix / 11.4.4：
U*-matrix / 11.4.5：
Gradient Fields / 11.4.6：
Component Planes / 11.4.7：
Smoothed Data Histograms / 11.4.8：
PlaySOM - Interaction with Music Maps / 11.5：
Interface / 11.5.1：
Interaction / 11.5.2：
Playlist Creation / 11.5.3：
PocketSOMPlayer - Music Retrieval on Mobile Devices / 11.6：
Playing Scenarios / 11.6.1：
Index / 11.6.3：
Introduction to Learning Principles for Multimedia Data / Part I：
Introduction to Bayesian Methods and Decision Theory / Simon P. Wilson ; Rozenn Dahyot ; Padraig Cunningham1：
Introduction / 1.1：
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