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1.

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1. System modeling in cell biology : from concepts to nuts and bolts ((ebook) :)
edited by Zoltan Szallasi, Jörg Stelling, Vipul Periwal
出版情報: Cambridge, Mass. ; London : MIT Press, c2006  1 online resource (xiv, 448 p.)
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Preface
General Concepts / I：
The Role of Modeling in Systems Biology / Douglas B. Kell ; Joshua D. Knowles1：
Complexity and Robustness of Cellular Systems / Jorg Stelling ; Uwe Sauer ; Francis J. Doyle III ; John Doyle2：
On Modules and Modularity / Zoltan Szallasi ; Vipul Periwal3：
Modeling Approaches / II：
Bayesian Inference of Biological Systems: The Logic of Biology / 4：
Stoichiometric and Constraint-based Modeling / Steffen Klamt5：
Modeling Molecular Interaction Networks with Nonlinear Ordinary Differential Equations / Emery D. Conrad ; John J. Tyson6：
Qualitative Approaches to the Analysis of Genetic Regulatory Networks / Hidde de Jong ; Delphine Ropers7：
Stochastic Modeling of Intracellular Kinetics / Johan Paulsson ; Johan Elf8：
Kinetics in Spatially Extended Systems / Karsten Kruse9：
Models And Reality / III：
Biological Data Acquisition for System Level Modeling-An Exercise in the Art of Compromise / 10：
Methods to Identify Cellular Architecture and Dynamics from Experimental Data / Rudiyanto Gunawan ; Kapil G. Gadker11：
Using Control Theory to Study Biology / Brian P. Ingalls ; Tau-Mi Yi ; Pablo A. Iglesias12：
Synthetic Gene Regulatory Systems / Mads Kaern ; Ron Weiss13：
Multilevel Modeling in Systems Biology: From Cells to Whole Organs / Denis Noble14：
Computational Modeling / IV：
Computational Constraints on Modeling in Systems Biology / 15：
Numerical Simulation for Biochemical Kinetics / Daniel T. Gillespie ; Linda R. Petzold16：
Software Infrastructure for Effective Communication and Reuse of Computational Models / Andrew Finney ; Michael Hucka ; Benjamin J. Bornstein ; Sarah M. Keating ; Bruce E. Shapiro ; Joanne Matthews ; Ben L. Kovitz ; Maria J. Schilstra ; Akira Funahashi ; Hiroaki Kitano17：
A Software Tools for Biological Modeling
References
Contributors
Index
Preface
General Concepts / I：
The Role of Modeling in Systems Biology / Douglas B. Kell ; Joshua D. Knowles1：
2.

電子ブック
ＥＢ
2. Does consciousness cause behavior? ((ebook) :)
edited by Susan Pockett, William P. Banks, and Shaun Gallagher
出版情報: Cambridge, Mass. ; London : MIT, c2006  1 online resource (vi, 364 p.)
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Introduction / Susan Pockett ; William P. Banks ; Shaun Gallagher
Neuroscience / I：
The Neuroscience of Movement / 1：
Consciousness of Action as an Embodied Consciousness / Marc Jeannerod2：
Intentions, Actions, and the Self / Suparna Choudhury ; Sarah-Jayne Blakemore3：
Free Choice and the Human Brain / Richard E. Passingham ; Hakwan C. Lau4：
Consciousness, Intentionality, and Causality / Walter J. Freeman5：
Philosophy / II：
Where's the Action? Epiphenomenalism and the Problem of Free Will / 6：
Empirical Constraints on the Problem of Free Will / Peter W. Ross7：
Toward a Dynamic Theory of Intentions / Elisabeth Pacherie8：
Phenomenology and the Feeling of Doing: Wegner on the Conscious Will / Timothy Bayne9：
Free Will: Theories, Analysis, and Data / Alfred R. Mele10：
Of Windmills and Straw Men: Folk Assumptions of Mind and Action / Bertram F. Malle11：
Law and Public Policy / III：
Does Consciousness Cause Misbehavior? / 12：
Free Will as a Social Institution / Wolfgang Prinz13：
Truth and/or Consequences: Neuroscience and Criminal Responsibility / Leonard V. Kaplan14：
Bypassing Conscious Control: Unconscious Imitation, Media Violence, and Freedom of Speech / Susan Hurley15：
Neurosciety Ahead? Debating Free Will in the Media / Sabine Maasen16：
List of Contributors
Index
Introduction / Susan Pockett ; William P. Banks ; Shaun Gallagher
Neuroscience / I：
The Neuroscience of Movement / 1：
3.

電子ブック
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3. Semi-supervised learning ((ebook) :)
[edited by] Olivier Chapelle, Bernhard Schölkopf, Alexander Zien
出版情報: Cambridge, Mass. ; London : MIT, c2006  1 online resource (x, 508 p.)
シリーズ名: Adaptive computation and machine learning ;
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Series Foreword
Preface
Introduction to Semi-Supervised Learning / 1：
Supervised, Unsupervised, and Semi-Supervised Learning / 1.1：
When Can Semi-Supervised Learning Work? / 1.2：
Classes of Algorithms and Organization of This Book / 1.3：
Generative Models / I：
A Taxonomy for Semi-Supervised Learning Methods / Matthias W. Seeger2：
The Semi-Supervised Learning Problem / 2.1：
Paradigms for Semi-Supervised Learning / 2.2：
Examples / 2.3：
Conclusions / 2.4：
Semi-Supervised Text Classification Using EM / N. C. Nigam ; Andrew McCallum ; Tom Mitchell3：
Introduction / 3.1：
A Generative Model for Text / 3.2：
Experminental Results with Basic EM / 3.3：
Using a More Expressive Generative Model / 3.4：
Overcoming the Challenges of Local Maxima / 3.5：
Conclusions and Summary / 3.6：
Risks of Semi-Supervised Learning / Fabio Cozman ; Ira Cohen4：
Do Unlabled Data Improve or Degrade Classification Performance? / 4.1：
Understanding Unlabeled Data: Asymptotic Bias / 4.2：
The Asymptotic Analysis of Generative Smei-Supervised Learning / 4.3：
The Value of Labeled and Unlabeled Data / 4.4：
Finite Sample Effects / 4.5：
Model Search and Robustness / 4.6：
Conclusion / 4.7：
Probabilistic Semi-Supervised Cluster with Constraints / Sugato Basu ; Mikhail Bilenko ; Arindam Banerjee ; Raymond J. Mooney5：
HMRF Model for Semi-Supervised Clustering / 5.1：
HMRF-KMeans Algorithm / 5.3：
Active Learning for Constraint Acquistion / 5.4：
Experimental Results / 5.5：
Related Work / 5.6：
Low-Density Separation / 5.7：
Transductive Support Vector Machines / Thorsten Joachims6：
Why Use Margin on the Test Set? / 6.1：
Experiments and Applications of the TSVMs / 6.4：
Solving the TSVM Optimization Problem / 6.5：
Connection to Related Approaches / 6.6：
Summary and Conclusions / 6.7：
Semi-Supervised Learning Using Semi-Definite Programming / Tijl De Bie ; Nello Cristianini7：
Relaxing SVM transduction / 7.1：
An Approximation for Speedup / 7.2：
General Semi-Supervised Learning Settings / 7.3：
Empirical Results / 7.4：
Summary and Outlook / 7.5：
Appendix
The Extended Schur Complement Lemma
Gaussian Processes and the Null-Category Noise Model / Neil D. Lawrence ; Michael I. Jordan8：
The Noise Model / 8.1：
Process Model and the Effect of the Null-Category / 8.3：
Posterior Inference and Prediction / 8.4：
Results / 8.5：
Discussion / 8.6：
Entropy Regularization / Yves Grandvalet ; Yoshua Bengio9：
Derivation of the Criterion / 9.1：
Optimization Algorithms / 9.3：
Related Methods / 9.4：
Experiments / 9.5：
Proof of Theorem 9.1 / 9.6：
Data-Dependent Regularization / Adrian Corduneanu ; Tommi S. Jaakkola10：
Information Regularization on Metric Spaces / 10.1：
Information Regularization and Relational Data / 10.3：
Graph-Based Models / 10.4：
Label Propogation and Quadratic Criterion / Olivier Delalleau ; Nicolas Le Roux11：
Label Propogation on a Similarity Graph / 11.1：
Quadratic Cost Criterion / 11.3：
From Transduction to Induction / 11.4：
Incorporating Class Prior Knowledge / 11.5：
Curse of Dimensionality for Semi-Supervised Learning / 11.6：
The Geometric Basis of Semi-Supervised Learning / Vikas Sindhwani ; Misha Belkin ; Partha Niyogi11.7：
Incorporating Geometry in Regularization / 12.1：
Algorithms / 12.3：
Data-Dependent Kernels for Semi-Supervised Learning / 12.4：
Linear Methods for Large-Scale Semi-Supervised Learning / 12.5：
Connections to Other Algorithms and Related Work / 12.6：
Future Directions / 12.7：
Discrete Regularization / Dengyong Zhou ; Bernhard Scholkopf13：
Discrete Analysis / 13.1：
Semi-Supervised Learning with Conditional Harmonic Mixing / Christopher J. C. Burges ; John C. Platt13.3：
Conditional Harmonic Mixing / 14.1：
Learning in CHM Models / 14.3：
Incorporating Prior Knowledge / 14.4：
Learning the Conditionals / 14.5：
Model Averaging / 14.6：
Change of Representation / 14.7：
Graph Kernels by Spectral Transforms / Xiaojin Zhu ; Jaz Kandola ; John Lafferty ; Zoubin Ghahramani15：
The Graph Laplacian / 15.1：
Kernels by Spectral Transforms / 15.2：
Kernel Alignment / 15.3：
Optimizing Alignment Using QCQP for Semi-Supervised Learning / 15.4：
Semi-Supervised Kernels with Order Restraints / 15.5：
Spectral Methods for Dimensionality Reduction / Lawrence K. Saul ; Kilian Weinberger ; Fei Sha ; Jihun Ham15.6：
Linear Methods / 16.1：
Graph-Based Methods / 16.3：
Kernel Methods / 16.4：
Modifying Distances / Alon Orlitsky ; Sajama16.5：
Estimating DBD Metrics / 17.1：
Computing DBD Metrics / 17.3：
Semi-Supervised Learning Using Density-Based Metrics / 17.4：
Conclusions and Future Work / 17.5：
Semi-Supervised Learning in Practice / V：
Large-Scale Algorithms / 18：
Cost Approximations / 18.1：
Subset Selection / 18.3：
Semi-Supervised Protein Classification Using Cluster Kernels / Jason Weston ; Christina Leslie ; Eugene Ie ; William Stafford Noble18.4：
Representation and Kernels for Protein Sequences / 19.1：
Semi-Supervised Kernels for Protein Sequences / 19.3：
Prediction of Protein Function from Networks / Hyunjung Shin ; Koji Tsuda19.4：
Graph-Based Semi-Supervised Learning / 20.1：
Combining Multiple Graphs / 20.3：
Experiments on Function Prediction of Proteins / 20.4：
Conclusion and Outlook / 20.5：
Analysis of Benchmarks / 21：
The Benchmark / 21.1：
Application of SSL Methods / 21.2：
Results and Discussion / 21.3：
Perspectives / VI：
An Augmented PAC Model for Semi-Supervised Learning / Maria-Florina Balcan ; Avrim Blum22：
A Formal Framework / 22.1：
Sample Complexity Results / 22.3：
Algorithmic Results / 22.4：
Related Models and Discussion / 22.5：
Metric-Based Approaches for Semi-Supervised Regression and Classification / Dale Schuurmans ; Finnegan Southey ; Dana Wilkinson ; Yuhong Guo23：
Metric Structure of Supervised Learning / 23.1：
Model Selection / 23.3：
Regularization / 23.4：
Classification / 23.5：
Transductive Inference and Semi-Supervised Learning / Vladimir Vapnik23.6：
Problem Settings / 24.1：
Problem of Generalization in Inductive and Transductive Inference / 24.2：
Structure of the VC Bounds and Transductive Inference / 24.3：
The Symmetrization Lemma and Transductive Inference / 24.4：
Bounds for Transductive Inference / 24.5：
The Structural Risk Minimization Principle for Induction and Transduction / 24.6：
Combinatorics in Transductive Inference / 24.7：
Measures of Size of Equivalence Classes / 24.8：
Algorithms for Inductive and Transductive SVMs / 24.9：
Semi-Supervised Learning / 24.10：
Conclusion: / 24.11：
Transductive Inference and the New Problems of Inference
Beyond Transduction: Selective Inference / 24.12：
A Discussion of Semi-Supervised Learning and Transduction / 25：
References
Notation and Symbols
Contributors
Index
Online Index
Series Foreword
Preface
Introduction to Semi-Supervised Learning / 1：
4.

電子ブック
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4. Lectures on quantum information /
edited by Dagmar Bruss and Gerd Leuchs
出版情報: Weinheim : Chichester : Wiley-VCH ; John Wiley [distributor], 2006  1 online resource
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5.

電子ブック
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5. The handbook of homogeneous hydrogenation /
Johannes G. de Vries, Cornelis J. Elsevier (eds.)
出版情報: Weinheim : WILEY-VCH, 2006  1 online resource (3 volumes (xxx, 1370 pages))
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6.

電子ブック
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6. Cyclodextrins and their complexes : : chemistry, analytical methods, applications (: electronic bk)
edited by Helena Dodziuk
出版情報: Weinheim : Wiley-VCH, 〓2006  1 online resource (xvii, 489 pages)
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Introduction
Reactivity and chemistry Polymers CyD Catalysis
Chromatography Enantioselective separations X-ray Calorimetry NMR
Other physicochemical methods: UV-vis, ICD, Electrochemistry, AFM and STM
Model calculations Rotaxane and catenane structures involving cyclodextrins
Large cyclodextrins Applications in pharmaceutical industry Cyclodextrin aggregates (simple and multiple emulsions, microparticles, nanoparticles, liposomes, niosomes)
Other applications: in cosmetic, toiletry, textile and wrapping industries; in agrochemistry; in electrochemical sensors and devices
Reactivity and chemistry Polymers CyD Catalysis Chromatography Enantioselective separations
X-ray Calorimetry NMR Other physicochemical methods: UV-vis, ICD, Electrochemistry, AFM and STM
Model calculations Rotaxane and catenane structures involving cyclodextrins Large cyclodextrins Applications in pharmaceutical industry Cyclodextrin aggregates (simple and multiple emulsions, microparticles, nanoparticles, liposomes, niosomes)
Introduction
Reactivity and chemistry Polymers CyD Catalysis
Chromatography Enantioselective separations X-ray Calorimetry NMR
7.

電子ブック
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7. R-Trees: Theory and Applications
Lakhmi Jain, Yannis Manolopoulos, Xindong Wu
出版情報: Springer eBooks Computer Science , Springer London, 2006
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8.

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8. Intelligent Spaces
A. J. Sammes, Alan Steventon, Steve Wright
出版情報: Springer eBooks Computer Science , Springer London, 2006
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Contributors
Introduction
Intelligent Spaces - The Vision, the Opportunities, and the Barriers / S Wright ; A Steventon1：
A Vision of Intelligent Spaces / 1.1：
Applications / 1.2：
Technology Capabilities / 1.3：
Roadmap to the Vision / 1.4：
Research Challenges / 1.5：
Summary / 1.6：
The Socio-Economic Impact of Pervasive Computing - Intelligent Spaces and the Organisation of Business / M H Lyons ; R Ellis ; J M M Potter ; D A M Holm ; R Venousiou2：
Commercial Opportunities / 2.1：
New Organisational Forms - The Emerging Value Nets / 2.3：
Creating the Adaptive Company / 2.4：
Changing the Way We Work / 2.5：
No Pervasive Computing Without Intelligent Systems / S G Thompson ; B Azvine2.6：
Needs Identification / 3.1：
Problems from Ubiquitous Computing - Solutions from Intelligent Systems Research / 3.3：
Component Understandability - Soft Computing / 3.4：
Component Adaptivity - Machine Learning / 3.5：
The Supply Chain / D Luckett3.6：
Introduction and Background to RFID / 4.1：
Retail/Supply Chain / 4.2：
What About the Consumer? / 4.3：
Care in the Community / S Brown ; N Hine ; A Sixsmith ; P Garner4.4：
The Concept of 'Well-Being' / 5.1：
How to Measure Changes in Well-Being / 5.3：
System Design, Deployment, and Service Issues / 5.4：
Summary and Key Technical Challenges / 5.5：
Pervasive Home Environments / P Bull ; R Limb ; R Payne6：
Vision / 6.1：
Technical Challenges / 6.3：
Traffimatics - Intelligent Co-operative Vehicle Highway Systems / G Bilchev ; D Marston ; N Hristov ; E Peytchev ; N Wall6.4：
Vision of Intelligent Co-operative Vehicle Highway Systems / 7.1：
Vision Implementation / 7.3：
Market Opportunities and Barriers / 7.4：
Mixed-Reality Applications in Urban Environments / J Bulman ; B Crabtree ; A Gower ; A Oldroyd ; J Sutton7.5：
3D Virtual-Reality and Mixed-Reality Scene Rendering / 8.1：
Pervasive Gaming - Gaming in Urban Environments / 8.3：
Workforce Management Application / 8.4：
Military Operations in Urban Environments / 8.5：
Future / 8.6：
A Sensor Network for Glaciers / K Martinez ; A Riddoch ; J Hart ; R Ong8.7：
The Glacsweb Project / 9.1：
System Architecture Version 2 / 9.3：
Example Results / 9.4：
Summary and Future Work / 9.5：
Co-operation in the Digital Age - Engendering Trust in Electronic Environments / A Seleznyov ; M O Ahmed ; S Hailes10：
Security Issues in Ubicomp / 10.1：
Decentralised Trust Management / 10.3：
ADAM / 10.4：
Maintaining Privacy in Pervasive Computing - Enabling Acceptance of Sensor-based Services / A Soppera ; T Burbridge10.5：
Emerging Pervasive Computing - Opportunities and Threats / 11.1：
Understanding Privacy in Pervasive Computing / 11.3：
Technical Approaches to Privacy / 11.4：
RFID Security and Privacy - Issues, Standards, and Solutions / D Molnar11.5：
RFID Tags Technology - An Overview / 12.1：
Privacy as a Multilayer Problem / 12.3：
Transfer of Ownership at the Application Level / 12.5：
Ambient Technology - Now You See It, Now You Don't / B MacDonald12.6：
Living in a Moore's Law World / 13.1：
Hardware Technology Influencers and Issues / 13.3：
The Key Hardware Technologies for Enabling iSpaces / 13.4：
Integrated Sensor Networks for Monitoring the Health and Well-Being of Vulnerable Individuals / D J T Heatley ; R S Kalawsky ; I Neild ; P A Bowman13.5：
Importance of Well-Being Care Provision / 14.1：
Activities of Daily Living / 14.3：
Ethical Considerations / 14.4：
Sensing Activities of Daily Living / 14.5：
Multiple Occupancy Issues / 14.6：
Sensor Fusion / 14.7：
Sensor Networks / 14.8：
Experimental Work / 14.9：
Segmentation and Tracking of Multiple Moving Objects for Intelligent Video Analysis / L-Q Xu ; J L Landabaso ; B Lei14.10：
Moving Objects Segmentation with Shadow Removal / 15.1：
Multi-Object Tracking Using Temporal Templates / 15.3：
Experimental Results / 15.4：
An Attention-based Approach to Content-based Image Retrieval / A Bamidele ; F W M Stentiford ; J Morphett15.5：
State of the Art / 16.1：
Current Research / 16.3：
Results / 16.4：
Discussion / 16.5：
Eye Tracking as a New Interface for Image Retrieval / O K Oyekoya16.6：
Current Research Objectives / 17.1：
The Implications of Pervasive Computing on Network Design / R Briscoe17.4：
Architecture / 18.1：
Component Services / 18.3：
Business Implications / 18.4：
Autonomic Computing for Pervasive ICT-A Whole-System Perspective / M Shackleton ; F Saffre ; R Tateson ; E Bonsma ; C Roadknight18.5：
Illustrative Example Systems / 19.1：
Discussion of Example Systems / 19.3：
The Need for 'Complex Systems' Theory and Modelling / 19.4：
Scale-Free Topology for Pervasive Networks / H Jovanovic ; C Hoile ; S Nicolas19.5：
Methodology / 20.1：
NEXUS-Resilient Intelligent Middleware / N Kaveh ; R Ghanea Hercock20.3：
Motivating Scenario / 21.1：
NEXUS Architecture / 21.3：
NEXUS Prototype / 21.4：
Related Work / 21.5：
Intelligent Data Analysis for Detecting Behaviour Patterns in iSpaces / D D Nauck ; B Majeed ; B-S Lee21.6：
Approaches to iSpaces / 22.1：
Intelligent Data Analysis in Sensor Networks / 22.3：
Detecting Unusual Patterns / 22.4：
xAssist-Inferring User Goals from Observed Actions / J Allen ; S Appleby ; G Churcher22.5：
Reasoning and Action Selection / 23.1：
xAssist Framework / 23.3：
Example xAssist Application / 23.4：
Programming iSpaces-A Tale of Two Paradigms / V Callaghan ; M Colley ; H Hagras ; J Chin ; F Doctor ; G Clarke23.5：
Degrees of Intelligence and Autonomy / 24.1：
The iDorm / 24.3：
Embedded Agents / 24.4：
Embedded-Agent-based Approaches / 24.5：
An End-User Programming-based Approach / 24.6：
Summary and Future Directions / 24.7：
Acronyms
Index
Contributors
Introduction
Intelligent Spaces - The Vision, the Opportunities, and the Barriers / S Wright ; A Steventon1：
9.

電子ブック
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9. Software Configuration Management Using Vesta
David Gries, Allan Heydon, Clark Allan Heydon, Fred B. Schneider
出版情報: Springer eBooks Computer Science , Springer US, 2006
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Preface
Introducing Vesta / Part I：
Introduction / 1：
Some Scenarios / 1.1：
The Configuration Management Challenge / 1.2：
The Vesta Response / 1.3：
Essential Background / 2：
The Unix File System / 2.1：
Naming Files and Directories / 2.1.1：
Mount Points / 2.1.2：
Links / 2.1.3：
Properties of Files / 2.1.4：
Unix Processes / 2.2：
The Unix Shell / 2.3：
The Unix Programming Environment / 2.4：
Make / 2.5：
The Architecture of Vesta / 3：
System Components / 3.1：
Source Management Components / 3.1.1：
Build Components / 3.1.2：
Storage Components / 3.1.3：
Models and Modularity / 3.1.4：
Vesta's Core Properties / 3.2：
The User's View of Vesta / Part II：
Managing Sources and Versions / 4：
Names and Versions / 4.1：
The Source Name Space / 4.1.1：
Versioning / 4.1.2：
Naming Files and Packages / 4.1.3：
The Development Cycle / 4.2：
The Outer Loop / 4.2.1：
The Inner Loop / 4.2.2：
Detailed Operation of the Repository Tools / 4.2.3：
Version Control Alternatives / 4.2.4：
Additional Repository Tools / 4.2.5：
Mutable Files and Directories / 4.2.6：
Replication / 4.3：
Global Name Space / 4.3.1：
A Replication Example / 4.3.2：
The Replicator / 4.3.3：
Cross-Repository Check-out / 4.3.4：
Repository Metadata / 4.4：
Mutable Attributes / 4.4.1：
Access Control / 4.4.2：
Metadata and Replication / 4.4.3：
System Description Language / 5：
Motivation / 5.1：
Language Highlights / 5.2：
The Environment Parameter / 5.2.1：
Bindings / 5.2.2：
Tool Encapsulation / 5.2.3：
Closures / 5.2.4：
Imports / 5.2.5：
Building Systems in Vesta / 6：
The Organization of System Models / 6.1：
Hierarchies of System Models / 6.2：
Bridges and the Standard Environment / 6.2.1：
Library Models / 6.2.2：
Application Models / 6.2.3：
Putting It All Together / 6.2.4：
Control Panel Models / 6.2.5：
Customizing the Build Process / 6.3：
Handling Large Scale Software / 6.4：
Inside Vesta / Part III：
Inside the Repository / 7：
Support for Evaluation and Caching / 7.1：
Derived Files and Shortids / 7.1.1：
Evaluator Directories and Volatile Directories / 7.1.2：
Fingerprints / 7.1.3：
Inside the Repository Implementation / 7.2：
Directory Implementation / 7.2.1：
Shortids and Files / 7.2.2：
Longids / 7.2.3：
Copy-on-Write / 7.2.4：
NFS Interface / 7.2.5：
RPC Interfaces / 7.2.6：
Implementing Replication / 7.3：
Mastership / 7.3.1：
Agreement / 7.3.2：
Agreement-Preserving Primitives / 7.3.3：
Propagating Attributes / 7.3.4：
Incremental Building / 8：
Overview of Function Caching / 8.1：
Caching and Dynamic Dependencies / 8.2：
The Function Cache Interface / 8.3：
Computing Fine-Grained Dependencies / 8.4：
Representing Dependencies / 8.4.1：
Caching External Tool Invocations / 8.4.2：
Caching User-Defined Function Evaluations / 8.4.3：
Caching System Model Evaluations: A Special Case / 8.4.4：
Error Handling / 8.5：
Function Cache Implementation / 8.6：
Cache Lookup / 8.6.1：
Cache Entry Storage / 8.6.2：
Synchronization / 8.6.3：
Evaluation and Caching in Action / 8.7：
Scratch Build of the Standard Environment / 8.7.1：
Scratch Build of the Vesta Umbrella Library / 8.7.2：
Scratch and Incremental Builds of the Evaluator / 8.7.3：
Weeder / 9：
How Deletion is Specified / 9.1：
Implementation of the Weeder / 9.2：
The Function Call Graph / 9.2.1：
Concurrent Weeding / 9.2.2：
Assessing Vesta / Part IV：
Competing Systems / 10：
Loosely Connected Configuration Management Tools / 10.1：
RCS / 10.1.1：
CVS / 10.1.2：
Integrated Configuration Management Systems / 10.1.3：
DSEE / 10.2.1：
ClearCASE / 10.2.2：
Other Systems / 10.3：
Vesta System Performance / 11：
Platform Configuration / 11.1：
Overall System Performance / 11.2：
Performance Comparison with Make / 11.2.1：
Performance Breakdown / 11.2.2：
Caching Analysis / 11.2.3：
Resource Usage / 11.2.4：
Repository Performance / 11.3：
Speed of File Operations / 11.3.1：
Disk and Memory Consumption / 11.3.2：
Speed of Repository Tools / 11.3.3：
Speed of Cross-Repository Tools / 11.3.4：
Speed of the Replicator / 11.3.5：
Function Cache Performance / 11.4：
Server Performance / 11.4.1：
Measurements of the Stable Cache / 11.4.2：
Disk and Memory Usage / 11.4.3：
Function Cache Scalability / 11.4.4：
Weeder Performance / 11.5：
Interprocess Communication / 11.6：
Conclusions / 12：
Vesta in the Real World / 12.1：
Vesta in the Future / 12.2：
SDL Reference Manual / A：
Lexical Conventions / A.1：
Meta-notation / A.2.1：
Terminals / A.2.2：
Semantics / A.3：
Value Space / A.3.1：
Type Declarations / A.3.2：
Evaluation Rules / A.3.3：
Expr / A.3.3.1：
Literal / A.3.3.2：
Id / A.3.3.3：
List / A.3.3.4：
Binding / A.3.3.5：
Select / A.3.3.6：
Block / A.3.3.7：
Stmt / A.3.3.8：
Assign / A.3.3.9：
Iterate / A.3.3.10：
FuncDef / A.3.3.11：
FuncCall / A.3.3.12：
Model / A.3.3.13：
Files / A.3.3.14：
File Name Interpretation / A.3.3.15：
Pragmas / A.3.3.17：
Primitives / A.3.4：
Functions on Type t_bool / A.3.4.1：
Functions on Type t_int / A.3.4.2：
Functions on Type t_text / A.3.4.3：
Functions on Type t_list / A.3.4.4：
Functions on Type t_binding / A.3.4.5：
Special Purpose Functions / A.3.4.6：
Type Manipulation Functions / A.3.4.7：
Tool Invocation Function / A.3.4.8：
Diagnostic Functions / A.3.4.9：
Concrete Syntax / A.4：
Grammar / A.4.1：
Ambiguity Resolution / A.4.2：
Tokens / A.4.3：
Reserved Identifiers / A.4.4：
The Vesta Web Site / B：
References
Index
Preface
Introducing Vesta / Part I：
Introduction / 1：
10.

電子ブック
ＥＢ
10. Estimation of Dependences Based on Empirical Data
Vladimir Naumovich Vapnik, S. Kotz, V. Vapnik
出版情報: Springer eBooks Computer Science , Springer New York, 2006
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目次情報: 続きを見る
he problem of estimating dependences from empirical data
ethods of expected-risk minimization
ethods of parametric statistics for the pattern recognition problem
ethods of parametric statistics for the problem of regression estimation
stimation of regression parameters
method of minimizing empirical risk for the problem of pattern recognition
method of minimizing empirical risk for the problem of regression estimation
he method of structural minimization of risk
olution of ill-posed problems, interpretation of measurements using the method of structural risk minimization
stimation of functional values at given points
ealism and instrumentalism: Classical statistics and the VC theory (1960-1980)
alsifiability and parsimony: VC dimension and the number of entities (1980-2000)
on-inductive methods of inference: Direct inference instead of generalization (2000- ...)
he big picture
he problem of estimating dependences from empirical data
ethods of expected-risk minimization
ethods of parametric statistics for the pattern recognition problem
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