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1.

図書
図書
1. Kohonen maps
edited by Erkki Oja and Samuel Kaski
出版情報: Amsterdam : Elsevier, 1999  ix, 390 p. ; 25 cm
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目次情報: 続きを見る
Selected papers only
Preface / Kohonen Maps
Analyzing and representing multidimentional quantitative and qualitative data: Demographic study of the / Rhône valley
The domeatic consumption of the Canadian families / M. Cottrell ; P. Gaubert ; P. Letremy ; P. Rousset
Value maps: Finding value in markets that are expensive / G.J. Deboeck
Data mining and knowledge discovery with emergent Self-Organizing Feature Maps for multivariate time series / A. Ultsch
Tree structured Self-Organizing Maps / P. Koikkalainen
On the optimization of Self-Organizing Maps by genetic algorithms / D. Polani
Self organization of a massive text document collection / T. Kohonen ; S. Kaski ; K. Lagus ; J. Salojárvi ; J. Honkela ; V. Paatero ; A. Saarela
Document classification with Self-Organizing Maps / D. Merkl
Navigation in databases using Self-Organizing Maps / S.A. Shumsky
Self-Organising Maps in computer aided design of electronic circuits / A. Hemani ; A. Postula
Modeling self-organization in the visual cortex / R. Miikkulainen ; J.A. Bednar ; Y. Choe ; J. Sirosh
A spatio-temporal memory based on SOMs with activity diffusion / N.R. Euliano ; J.C. Principe
Advances in modeling cortical maps / P.G. Morasso ; V. Sanguineti ; F. Frisone
Topology preservation in Self-Organizing Maps / T. Villmann
Second-order learing in Self-Organizing Maps / R. Der ; M. Herrmann
Energy functions for Self-Organizing Maps / T. Heskes
LVQ and single trial EEG classification / G. Pfurtscheller ; M. Pregenzer
Self-Organizing Map in categorization of voice qualities / L. Leinonen
Self-Organizing Map in analysis of large-scale industrial systems / O. Simula ; J. Ahola ; E. Alhoniemi ; J. Himberg ; J. Vesanto
Keyword index
Selected papers only
Preface / Kohonen Maps
Analyzing and representing multidimentional quantitative and qualitative data: Demographic study of the / Rhône valley
2.

図書
図書
2. Computational intelligence : an introduction
Witold Pedrycz
出版情報: Boca Raton, Fla. : CRC Press, c1998  284 p. ; 26 cm
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3.

図書
図書
3. Bioinformatics : the machine learning approach (: hc)
Pierre Baldi, Søren Brunak
出版情報: Cambridge, Mass. : The MIT Press, 1998  xviii, 351 p., [8] p. of plats ; 24 cm
シリーズ名: Adaptive computation and machine learning
Bradford book
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目次情報: 続きを見る
Series Foreword
Preface
Introduction / 1：
Biological Data in Digital Symbol Sequences / 1.1：
Genomes--Diversity, Size, and Structure / 1.2：
Proteins and Proteomes / 1.3：
On the Information Content of Biological Sequences / 1.4：
Prediction of Molecular Function and Structure / 1.5：
Machine Learning Foundations: The Probabilistic Framework / 2：
Introduction: Bayesian Modeling / 2.1：
The Cox-Jaynes Axioms / 2.2：
Bayesian Inference and Induction / 2.3：
Model Structures: Graphical Models and Other Tricks / 2.4：
Summary / 2.5：
Probabilistic Modeling and Inference: Examples / 3：
The Simplest Sequence Models / 3.1：
Statistical Mechanics / 3.2：
Machine Learning Algorithms / 4：
Dynamic Programming / 4.1：
Gradient Descent / 4.3：
EM/GEM Algorithms / 4.4：
Markov Chain Monte Carlo Methods / 4.5：
Simulated Annealing / 4.6：
Evolutionary and Genetic Algorithms / 4.7：
Learning Algorithms: Miscellaneous Aspects / 4.8：
Neural Networks: The Theory / 5：
Universal Approximation Properties / 5.1：
Priors and Likelihoods / 5.3：
Learning Algorithms: Backpropagation / 5.4：
Neural Networks: Applications / 6：
Sequence Encoding and Output Interpretation / 6.1：
Prediction of Protein Secondary Structure / 6.2：
Prediction of Signal Peptides and Their Cleavage Sites / 6.3：
Applications for DNA and RNA Nucleotide Sequences / 6.4：
Hidden Markov Models: The Theory / 7：
Prior Information and Initialization / 7.1：
Likelihood and Basic Algorithms / 7.3：
Learning Algorithms / 7.4：
Applications of HMMs: General Aspects / 7.5：
Hidden Markov Models: Applications / 8：
Protein Applications / 8.1：
DNA and RNA Applications / 8.2：
Conclusion: Advantages and Limitations of HMMs / 8.3：
Hybrid Systems: Hidden Markov Models and Neural Networks / 9：
Introduction to Hybrid Models / 9.1：
The Single-Model Case / 9.2：
The Multiple-Model Case / 9.3：
Simulation Results / 9.4：
Probabilistic Models of Evolution: Phylogenetic Trees / 9.5：
Introduction to Probabilistic Models of Evolution / 10.1：
Substitution Probabilities and Evolutionary Rates / 10.2：
Rates of Evolution / 10.3：
Data Likelihood / 10.4：
Optimal Trees and Learning / 10.5：
Parsimony / 10.6：
Extensions / 10.7：
Stochastic Grammars and Linguistics / 11：
Introduction to Formal Grammars / 11.1：
Formal Grammars and the Chomsky Hierarchy / 11.2：
Applications of Grammars to Biological Sequences / 11.3：
Likelihood / 11.4：
Applications of SCFGs / 11.6：
Experiments / 11.8：
Future Directions / 11.9：
Internet Resources and Public Databases / 12：
A Rapidly Changing Set of Resources / 12.1：
Databases over Databases and Tools / 12.2：
Databases over Databases / 12.3：
Databases / 12.4：
Sequence Similarity Searches / 12.5：
Alignment / 12.6：
Selected Prediction Servers / 12.7：
Molecular Biology Software Links / 12.8：
Ph.D. Courses over the Internet / 12.9：
HMM/NN Simulator / 12.10：
Statistics / A：
Decision Theory and Loss Functions / A.1：
Quadratic Loss Functions / A.2：
The Bias/Variance Trade-off / A.3：
Combining Estimators / A.4：
Error Bars / A.5：
Sufficient Statistics / A.6：
Exponential Family / A.7：
Gaussian Process Models / A.8：
Variational Methods / A.9：
Information Theory, Entropy, and Relative Entropy / B：
Entropy / B.1：
Relative Entropy / B.2：
Mutual Information / B.3：
Jensen's Inequality / B.4：
Maximum Entropy / B.5：
Minimum Relative Entropy / B.6：
Probabilistic Graphical Models / C：
Notation and Preliminaries / C.1：
The Undirected Case: Markov Random Fields / C.2：
The Directed Case: Bayesian Networks / C.3：
HMM Technicalities, Scaling, Periodic Architectures, State Functions, and Dirichlet Mixtures / D：
Scaling / D.1：
Periodic Architectures / D.2：
State Functions: Bendability / D.3：
Dirichlet Mixtures / D.4：
List of Main Symbols and Abbreviations / E：
References
Index
Series Foreword
Preface
Introduction / 1：
4.

図書
図書
4. Algorithms and architectures
edited by Cornelius T. Leondes
出版情報: San Diego : Academic Press, c1998  xxix, 460 p. ; 24 cm
シリーズ名: Neural network systems techniques and applications ; vol. 1
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5.

図書
図書
5. Neural systems for robotics
edited by Omid Omidvar, Patrick van der Smagt
出版情報: San Diego ; Tokyo : Academic Press, c1997  xvii, 346 p. ; 24 cm
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Neural Network Sonar as a Perceptual Modality for Robotics / W.T. Miller III ; A.L. Kun,lt;/i>
Dynamic Balance of a Biped Walking / Robot. P. van der Smagt ; F. Groen,lt;/i>
Visual Feedback in Motion / D. DeMers ; K. Kreutz-Delgado,lt;/i>
Inverse Kinematics of Dextrous Manipulators / Y. Jin, T. Pipe ; A. Winfield,lt;/i>
Stable Manipulator Trajectory Control Using Neural Networks / P. Gaudiano ; F.H. Guenther ; E. Zalama,lt;/i>
The Neural Dynamics Approach to Sensory-Motor Control / A. Buhlmeier ; G. Maneuffel,lt;/i>
Operant Conditioning in Robots / B. Hallam ; J. Hallam ; G. Hayes,lt;/i>
A Dynamic Net for Robot Control / Ben Krise ; J. van Dam,lt;/i>
Neural Vehicles / J. Heikkonen ; P. Koikkalainen,lt;/i>
Self-Organization and Autonomous Robots
Neural Network Sonar as a Perceptual Modality for Robotics / W.T. Miller III ; A.L. Kun,lt;/i>
Dynamic Balance of a Biped Walking / Robot. P. van der Smagt ; F. Groen,lt;/i>
Visual Feedback in Motion / D. DeMers ; K. Kreutz-Delgado,lt;/i>
6.

図書
図書
6. The book of GENESIS : exploring realistic neural models with the General Neural Simulation System (: [us] ; : [gw])
James M. Bower and David Beeman
出版情報: Santa Clara, Calif. : TELOS, Springer-Verlag, c1995  xx, 409 p. ; 24 cm
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7.

図書
図書
7. Neural networks for robotic control : theory and applications
edited by A.M.S. Zalzala and A.S. Morris
出版情報: New York ; Tokyo : Ellis Horwood, 1996  viii, 278 p. ; 25 cm
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8.

図書
図書
8. Neural network engineering in dynamic control systems
Kenneth Hunt, George Irwin and Kevin Warwick (eds.)
出版情報: Berlin ; New York : Springer, c1995  278 p. ; 24 cm
シリーズ名: Advances in industrial control
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9.

図書
図書
9. Pattern recognition and neural networks
B.D. Ripley
出版情報: New York : Cambridge University Press, 1996  xi, 403 p. ; 26 cm
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目次情報: 続きを見る
Introduction and examples / 1：
Statistical decision theory / 2：
Linear discriminant analysis / 3：
Flexible discriminants / 4：
Feed-forward neural networks / 5：
Non-parametric methods / 6：
Tree-structured classifiers / 7：
Belief networks / 8：
Unsupervised methods / 9：
Finding good pattern features / 10：
statistical sidelines / Appendix：
Glossary
References
Author index
Subject index
Introduction and examples / 1：
Statistical decision theory / 2：
Linear discriminant analysis / 3：
10.

図書
図書
10. Neural network fundamentals with graphs, algorithms, and applications (: hard)
N. K. Bose, P. Liang
出版情報: New York ; Tokyo : McGraw-Hill, c1996  xxxiii, 478 p. ; 25 cm
シリーズ名: McGraw-Hill series in electrical and computer engineering ; . Communications and signal processing
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11.

図書
図書
11. Feed-forward neural networks : vector decomposition analysis, modelling and analog implementation
by Anne-Johan Annema
出版情報: Boston : Kluwer Academic Publishers, c1995  xiii, 238 p. ; 25 cm
シリーズ名: The Kluwer international series in engineering and computer science ; Analog circuits and signal processing
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12.

図書
図書
12. Sensory neural networks : lateral inhibition
Bahram Nabet, Robert B. Pinter
出版情報: Boca Raton : CRC Press, c1991  xi, 182 p. ; 25 cm
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13.

図書
図書
13. Neural networks for vision and image processing (: pbk)
edited by Gail A. Carpenter and Stephen Grossberg
出版情報: Cambridge, Mass. ; London : MIT Press, c1992  467 p. ; 26 cm
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14.

図書
図書
14. Modular learning in neural networks : a modularized approach to neural network classification
Tomas Hrycej
出版情報: New York, NY : Wiley, c1992  xiii, 235 p. ; 25 cm
シリーズ名: Sixth-generation computer technology series
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15.

図書
図書
15. Neural networks : concepts, applications, and implementations (Vol. 1 - Vol. 4)
Paolo Antognetti and Veljko Milutinović, editors
出版情報: Englewood Cliffs, N.J. : Prentice Hall, 1991  4 v. ; 24 cm
シリーズ名: Prentice Hall advanced reference series ; . Engineering
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16.

図書
図書
16. Neural networks and speech processing
by David P. Morgan, Christopher L. Scofield ; foreword by Leon N. Cooper
出版情報: Boston : Kluwer Academic Publishers, c1991  xvi, 391 p. ; 25 cm
シリーズ名: The Kluwer international series in engineering and computer science ; . VLSI, computer architecture, and digital signal processing
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17.

図書
図書
17. Computation, learning, and architectures
edited by Harry Wechsler
出版情報: Boston : Academic Press, c1992  xix, 363 p. ; 24 cm
シリーズ名: Neural networks for perception / edited by Harry Wechsler ; v. 2
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18.

図書
図書
18. Artificial neural networks for computer vision (: us ; : gw)
Yi-Tong Zhou, Rama Chellappa
出版情報: New York ; Tokyo : Springer-Verlag, c1992  xi, 170 p. ; 24 cm
シリーズ名: Research notes in neural computing ; v. 5
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19.

図書
図書
19. Artificial neural networks : approximation and learning theory
Halbert White with A.R. Gallant ... [et al.]
出版情報: Cambridge, Mass., USA ; Oxford : Blackwell, 1992  x, 329 p.
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20.

図書
図書
20. Natural and artificial parallel computation
edited by M.A. Arbib and J.A. Robinson
出版情報: Cambridge, MA : MIT Press, c1990  x, 345 p. ; 24 cm
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21.

図書
図書
21. Connectionist speech recognition : a hybrid approach
by Hervé A. Bourlard, Nelson Morgan ; foreword by Richard Lippmann
出版情報: Boston : Kluwer Academic Publishers, c1994  xxviii, 312 p. ; 25 cm
シリーズ名: The Kluwer international series in engineering and computer science ; SECS 247 . VLSI, computer architecture, and digital signal processing
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目次情報: 続きを見る
List of Figures
List of Tables
Notation
Foreword
Preface
Background / I：
Introduction / 1：
Statistical Pattern Classification / 2：
Hidden Markov Models / 3：
Multilayer Perceptions / 4：
Hybrid HMM/MLP Systems / II：
Speech Recognition using ANNs / 5：
Statistical Inference in MLPs / 6：
The Hybrid HMM/MLP Approach / 7：
Experimental Systems / 8：
Context-Dependent MPLs / 9：
System Tradeoffs / 10：
Training Hardware and Software / 11：
Additional Topics / III：
Cross-Validation in MLP Training / 12：
HMM/MLP and Predictive Models / 13：
Feature Extraction by MLP / 14：
Finale / IV：
Final System Overview / 15：
Conclusions / 16：
Bibliography
Index
Acronyms
List of Figures
List of Tables
Notation
22.

図書
図書
22. Artificial neural networks for speech and vision
edited by Richard J. Mammone
出版情報: London ; New York : Chapman & Hall, 1994  xx, 586 p.
シリーズ名: Chapman & Hall neural computing ; 4
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23.

図書
図書
23. Neural networks for pattern recognition
Albert Nigrin
出版情報: Cambridge, Mass. : MIT Press, c1993  xvii, 413 p. ; 24 cm
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24.

図書
図書
24. The roots of backpropagation : from ordered derivatives to neural networks and political forecasting (cloth:alk.paper)
Paul John Werbos
出版情報: New York : J. Wiley & Sons, c1994  xii, 319 p. ; 25 cm
シリーズ名: Adaptive and learning systems for signal processing, communications, and control
A Wiley-Interscience publication
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目次情報: 続きを見る
Thesis
Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences
Dynamic Feedback, Statistical Estimation, and Systems Optimization: General Techniques
The Multivariate ARMA(1,1) Model: Its Significance and Estimation
Simulation Studies of Techniques of Time-Series Analysis
General Applications of These Ideas: Practical Hazards and New Possibilities
Nationalism and Social Communications: A Test Case for Mathematical Approaches
Applications and Extensions
Forms of Backpropagation for Sensitivity Analysis, Optimization, and Neural Networks
Backpropagation Through Time: What It Does and How to Do It
Neurocontrol: Where It Is Going and Why It Is Crucial
Neural Networks and the Human Mind: New Mathematics Fits Humanistic Insight
Index
Thesis
Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences
Dynamic Feedback, Statistical Estimation, and Systems Optimization: General Techniques
25.

図書
図書
25. Techniques and applications of neural networks
P.J.G. Lisboa, M.J. Taylor
出版情報: New York : Ellis Horwood, 1993  307 p. ; 25 cm
シリーズ名: Ellis Horwood workshop series
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26.

図書
図書
26. Dynamical systems with saturation nonlinearities : analysis and design (: gw ; : us)
Derong Liu and Anthony N. Michel
出版情報: London ; Berlin ; New York : Springer-Verlag, c1994  xiv, 191 p. ; 24 cm
シリーズ名: Lecture notes in control and information sciences ; 195
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27.

図書
図書
27. Applications of neural networks
edited by Alan F. Murray
出版情報: Dordrecht ; Boston : Kluwer Academic Publishers, c1995  xii, 322 p. ; 25 cm
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28.

図書
図書
28. Neural information processing and VLSI
by Bing J. Sheu, Joongho Choi ; with special assistance from Robert C. Chang ... [et al.]
出版情報: Boston : Kluwer Academic Publishers, c1995  xix, 559 p. ; 25 cm
シリーズ名: The Kluwer international series in engineering and computer science ; SECS 304
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29.

図書
図書
29. Neural network learning and expert systems
Stephen I. Gallant
出版情報: Cambridge, Mass. : MIT Press, c1993  xvi, 365 p. ; 24 cm
シリーズ名: Bradford book
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目次情報: 続きを見る
Foreword
Basics / I：
Introduction and Important Definitions / 1：
Why Connectionist Models? / 1.1：
The Grand Goals of Al and Its Current Impasse / 1.1.1：
The Computational Appeal of Neural Networks / 1.1.2：
The Structure of Connectionist Models / 1.2：
Network Properties / 1.2.1：
Cell Properties / 1.2.2：
Dynamic Properties / 1.2.3：
Learning Properties / 1.2.4：
Two Fundamental Models: Multilayer Perceptrons (MLP's) and Backpropagation Networks (BPN's) / 1.3：
Multilayer Perceptrons (MLP's) / 1.3.1：
Backpropagation Networks (BPN's) / 1.3.2：
Gradient Descent / 1.4：
The Algorithm / 1.4.1：
Practical Problems / 1.4.2：
Comments / 1.4.3：
Historic and Bibliographic Notes / 1.5：
Early Work / 1.5.1：
The Decline of the Perceptron / 1.5.2：
The Rise of Connectionist Research / 1.5.3：
Other Bibliographic Notes / 1.5.4：
Exercises / 1.6：
Programming Project / 1.7：
Representation Issues / 2：
Representing Boolean Functions / 2.1：
Equivalence of {+1, -1,0} and {1,0} Forms / 2.1.1：
Single-Cell Models / 2.1.2：
Nonseparable Functions / 2.1.3：
Representing Arbitrary Boolean Functions / 2.1.4：
Representing Boolean Functions Using Continuous Connectionist Models / 2.1.5：
Distributed Representations / 2.2：
Definition / 2.2.1：
Storage Efficiency and Resistance to Error / 2.2.2：
Superposition / 2.2.3：
Learning / 2.2.4：
Feature Spaces and ISA Relations / 2.3：
Feature Spaces / 2.3.1：
Concept-Function Unification / 2.3.2：
ISA Relations / 2.3.3：
Binding / 2.3.4：
Representing Real-Valued Functions / 2.4：
Approximating Real Numbers by Collections of Discrete Cells / 2.4.1：
Precision / 2.4.2：
Approximating Real Numbers by Collections of Continuous Cells / 2.4.3：
Example: Taxtime! / 2.5：
Programming Projects / 2.6：
Learning In Single-Layer Models / II：
Perceptron Learning and the Pocket Algorithm / 3：
Perceptron Learning for Separable Sets of Training Examples / 3.1：
Statement of the Problem / 3.1.1：
Computing the Bias / 3.1.2：
The Perceptron Learning Algorithm / 3.1.3：
Perceptron Convergence Theorem / 3.1.4：
The Perceptron Cycling Theorem / 3.1.5：
The Pocket Algorithm for Nonseparable Sets of Training Examples / 3.2：
Problem Statement / 3.2.1：
Perceptron Learning Is Poorly Behaved / 3.2.2：
The Pocket Algorithm / 3.2.3：
Ratchets / 3.2.4：
Examples / 3.2.5：
Noisy and Contradictory Sets of Training Examples / 3.2.6：
Rules / 3.2.7：
Implementation Considerations / 3.2.8：
Proof of the Pocket Convergence Theorem / 3.2.9：
Khachiyan's Linear Programming Algorithm / 3.3：
Winner-Take-All Groups or Linear Machines / 3.4：
Generalizes Single-Cell Models / 4.1：
Perceptron Learning for Winner-Take-All Groups / 4.2：
The Pocket Algorithm for Winner-Take-All Groups / 4.3：
Kessler's Construction, Perceptron Cycling, and the Pocket Algorithm Proof / 4.4：
Independent Training / 4.5：
Autoassociators and One-Shot Learning / 4.6：
Linear Autoassociators and the Outer-Product Training Rule / 5.1：
Anderson's BSB Model / 5.2：
Hopfieid's Model / 5.3：
Energy / 5.3.1：
The Traveling Salesman Problem / 5.4：
The Cohen-Grossberg Theorem / 5.5：
Kanerva's Model / 5.6：
Autoassociative Filtering for Feedforward Networks / 5.7：
Concluding Remarks / 5.8：
Mean Squared Error (MSE) Algorithms / 5.9：
Motivation / 6.1：
MSE Approximations / 6.2：
The Widrow-Hoff Rule or LMS Algorithm / 6.3：
Number of Training Examples Required / 6.3.1：
Adaline / 6.4：
Adaptive Noise Cancellation / 6.5：
Decision-Directed Learning / 6.6：
Unsupervised Learning / 6.7：
Introduction / 7.1：
No Teacher / 7.1.1：
Clustering Algorithms / 7.1.2：
k-Means Clustering / 7.2：
Topology-Preserving Maps / 7.2.1：
Example / 7.3.1：
Demonstrations / 7.3.4：
Dimensionality, Neighborhood Size, and Final Comments / 7.3.5：
Art1 / 7.4：
Important Aspects of the Algorithm / 7.4.1：
Art2 / 7.4.2：
Using Clustering Algorithms for Supervised Learning / 7.6：
Labeling Clusters / 7.6.1：
ARTMAP or Supervised ART / 7.6.2：
Learning In Multilayer Models / 7.7：
The Distributed Method and Radial Basis Functions / 8：
Rosenblatt's Approach / 8.1：
The Distributed Method / 8.2：
Cover's Formula / 8.2.1：
Robustness-Preserving Functions / 8.2.2：
Hepatobiliary Data / 8.3：
Artificial Data / 8.3.2：
How Many Cells? / 8.4：
Pruning Data / 8.4.1：
Leave-One-Out / 8.4.2：
Radial Basis Functions / 8.5：
A Variant: The Anchor Algorithm / 8.6：
Scaling, Multiple Outputs, and Parallelism / 8.7：
Scaling Properties / 8.7.1：
Multiple Outputs and Parallelism / 8.7.2：
A Computational Speedup for Learning / 8.7.3：
Computational Learning Theory and the BRD Algorithm / 8.7.4：
Introduction to Computational Learning Theory / 9.1：
PAC-Learning / 9.1.1：
Bounded Distributed Connectionist Networks / 9.1.2：
Probabilistic Bounded Distributed Concepts / 9.1.3：
A Learning Algorithm for Probabilistic Bounded Distributed Concepts / 9.2：
The BRD Theorem / 9.3：
Polynomial Learning / 9.3.1：
Noisy Data and Fallback Estimates / 9.4：
Vapnik-Chervonenkis Bounds / 9.4.1：
Hoeffding and Chernoff Bounds / 9.4.2：
Pocket Algorithm / 9.4.3：
Additional Training Examples / 9.4.4：
Bounds for Single-Layer Algorithms / 9.5：
Fitting Data by Limiting the Number of Iterations / 9.6：
Discussion / 9.7：
Exercise / 9.8：
Constructive Algorithms / 9.9：
The Tower and Pyramid Algorithms / 10.1：
The Tower Algorithm / 10.1.1：
Proof of Convergence / 10.1.2：
A Computational Speedup / 10.1.4：
The Pyramid Algorithm / 10.1.5：
The Cascade-Correlation Algorithm / 10.2：
The Tiling Algorithm / 10.3：
The Upstart Algorithm / 10.4：
Other Constructive Algorithms and Pruning / 10.5：
Easy Learning Problems / 10.6：
Decomposition / 10.6.1：
Expandable Network Problems / 10.6.2：
Limits of Easy Learning / 10.6.3：
Backpropagation / 10.7：
The Backpropagation Algorithm / 11.1：
Statement of the Algorithm / 11.1.1：
A Numerical Example / 11.1.2：
Derivation / 11.2：
Practical Considerations / 11.3：
Determination of Correct Outputs / 11.3.1：
Initial Weights / 11.3.2：
Choice of r / 11.3.3：
Momentum / 11.3.4：
Network Topology / 11.3.5：
Local Minima / 11.3.6：
Activations in [0,1] versus [-1, 1] / 11.3.7：
Update after Every Training Example / 11.3.8：
Other Squashing Functions / 11.3.9：
NP-Completeness / 11.4：
Overuse / 11.5：
Interesting Intermediate Cells / 11.5.2：
Continuous Outputs / 11.5.3：
Probability Outputs / 11.5.4：
Using Backpropagation to Train Multilayer Perceptrons / 11.5.5：
Backpropagation: Variations and Applications / 11.6：
NETtalk / 12.1：
Input and Output Representations / 12.1.1：
Experiments / 12.1.2：
Backpropagation through Time / 12.1.3：
Handwritten Character Recognition / 12.3：
Neocognitron Architecture / 12.3.1：
The Network / 12.3.2：
Robot Manipulator with Excess Degrees of Freedom / 12.3.3：
The Problem / 12.4.1：
Training the Inverse Network / 12.4.2：
Plan Units / 12.4.3：
Simulated Annealing and Boltzmann Machines / 12.4.4：
Simulated Annealing / 13.1：
Boltzmann Machines / 13.2：
The Boltzmann Model / 13.2.1：
Boltzmann Learning / 13.2.2：
The Boltzmann Algorithm and Noise Clamping / 13.2.3：
Example: The 4-2-4 Encoder Problem / 13.2.4：
Remarks / 13.3：
Neural Network Expert Systems / 13.4：
Expert Systems and Neural Networks / 14：
Expert Systems / 14.1：
What Is an Expert System? / 14.1.1：
Why Expert Systems? / 14.1.2：
Historically Important Expert Systems / 14.1.3：
Critique of Conventional Expert Systems / 14.1.4：
Neural Network Decision Systems / 14.2：
Example: Diagnosis of Acute Coronary Occlusion / 14.2.1：
Example: Autonomous Navigation / 14.2.2：
Other Examples / 14.2.3：
Decision Systems versus Expert Systems / 14.2.4：
MACIE, and an Example Problem / 14.3：
Diagnosis and Treatment of Acute Sarcophagal Disease / 14.3.1：
Network Generation / 14.3.2：
Sample Run of Macie / 14.3.3：
Real-Valued Variables and Winner-Take-All Groups / 14.3.4：
Not-Yet-Known versus Unavailable Variables / 14.3.5：
Applicability of Neural Network Expert Systems / 14.4：
Details of the MACIE System / 14.5：
Inferencing and Forward Chaining / 15.1：
Discrete Multilayer Perceptron Models / 15.1.1：
Continuous Variables / 15.1.2：
Winner-Take-All Groups / 15.1.3：
Using Prior Probabilities for More Aggressive Inferencing / 15.1.4：
Confidence Estimation / 15.2：
A Confidence Heuristic Prior to Inference / 15.2.1：
Confidence in Inferences / 15.2.2：
Information Acquisition and Backward Chaining / 15.3：
Concluding Comment / 15.4：
Noise, Redundancy, Fault Detection, and Bayesian Decision Theory / 15.5：
The High Tech Lemonade Corporation's Problem / 16.1：
The Deep Model and the Noise Model / 16.2：
Generating the Expert System / 16.3：
Probabilistic Analysis / 16.4：
Noisy Single-Pattern Boolean Fault Detection Problems / 16.5：
Convergence Theorem / 16.6：
Extracting Rules from networks / 16.7：
Why Rules? / 17.1：
What Kind of Rules? / 17.2：
Criteria / 17.2.1：
Inference Justifications versus Rule Sets / 17.2.2：
Which Variables in Conditions / 17.2.3：
Inference Justifications / 17.3：
MACIE's Algorithm / 17.3.1：
The Removal Algorithm / 17.3.2：
Key Factor Justifications / 17.3.3：
Justifications for Continuous Models / 17.3.4：
Rule Sets / 17.4：
Limiting the Number of Conditions / 17.4.1：
Approximating Rules / 17.4.2：
Conventional + Neural Network Expert Systems / 17.5：
Debugging an Expert System Knowledge Base / 17.5.1：
The Short-Rule Debugging Cycle / 17.5.2：
Appendix Representation Comparisons / 17.6：
DNF Expressions / A.1 DNF Expressions and Polynomial Representability：
Polynomial Representability / A.1.2：
Space Comparison of MLP and DNF Representations / A.1.3：
Speed Comparison of MLP and DNF Representations / A.1.4：
MLP versus DNF Representations / A.1.5：
Decision Trees / A.2：
Representing Decision Trees by MLP's / A.2.1：
Speed Comparison / A.2.2：
Decision Trees versus MLP's / A.2.3：
p-lDiagrams / A.3：
Symmetric Functions and Depth Complexity / A.4：
Bibliography / A.5：
Index
Foreword
Basics / I：
Introduction and Important Definitions / 1：
30.

図書
図書
30. Hybrid neural network and expert systems
Larry R. Medsker
出版情報: Boston : Kluwer Academic, c1994  240 p. ; 25 cm
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31.

図書
図書
31. Neural networks for signal processing (: pbk)
Bart Kosko, editor
出版情報: Englewood Cliffs, N.J. : Prentice Hall, c1992  xv, 399 p. ; 25 cm
所蔵情報: loading…
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