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1.

図書
図書
1. Adaptive processing of sequences and data structures : International Summer School on Neural Networks "E.R. Caianiello", Vietri sul Mare, Salerno, Italy, September 6-13, 1997, tutorial lectures
C. Lee Giles, Marco Gori, eds
出版情報: Berlin ; New York : Springer, c1998  xii, 434 p. ; 24 cm
シリーズ名: Lecture notes in computer science ; 1387 . Lecture notes in artificial intelligence
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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. Neural networks and pattern recognition
edited by Omid Omidvar, Judith Dayhoff
出版情報: San Diego, Calif. : Academic Press, c1998  xvi, 351 p. ; 24 cm
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4.

図書
図書
4. Neural networks : tricks of the trade
Genevieve B. Orr, Klaus-Robert Müller (eds.)
出版情報: Berlin : Springer, c1998  vi, 432 p. ; 24 cm
シリーズ名: Lecture notes in computer science ; 1524
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目次情報: 続きを見る
Introduction
Speeding Learning
Preface
Efficient BackProp / Yann LeCun ; Leon Bottou ; Genevieve B. Orr ; Klaus-Robert Müller1：
Regularization Techniques to Improve Generalization
Early Stopping - But When? / Lutz Prechelt2：
A Simple Trick for Estimating the Weight Decay Parameter / Thorsteinn S. Rögnvaldsson3：
Controling the Hyperparameter Search in MacKay's Bayesian Neural Network Framework / Tony Plate4：
Adaptive Regularization in Neural Network Modeling / Jan Larsen ; Claus Svarer ; Lars Nonboe Andersen ; Lars Kai Han- sen5：
Large Ensemble Averaging / David Horn ; Ury Naftaly ; Nathan Intrator6：
Improving Network Models and Algorithmic Tricks
Square Unit Augmented, Radially Extended, Multilayer Perceptrons / Gary William Flake7：
A Dozen Tricks with Multitask Learning / Rich Caruana8：
Solving the Ill-Conditioning in Neural Network Learning / Patrick van der Smagt ; Gerd Hirzinger9：
Centering Neural Network Gradient Factors / Nicol N. Schraudolph10：
Avoiding Roundoff Error in Backpropagating Derivatives / 11：
Representing and Incorporating Prior Knowledge in Neural Network Training
Transformation Invariance in Pattern Recognition - Tangent Distance and Tangent Propagation / Patrice Y. Simard ; Yann A. LeCun ; John S. Denker ; Bernard Victorri12：
Combining Neural Networks and Context-Driven Search for On-Line, Printed Handwriting Recognition in the Newton / Larry S. Yaeger ; Brandyn J. Webb ; Richard F. Lyon13：
Neural Network Classification and Prior Class Probabilities / Steve Lawrence ; Ian Burns ; Andrew Back ; Ah Chung Tsoi ; C. Lee Gi- les14：
Applying Divide and Conquer to Large Scale Pattern Recognition Tasks / Jurgen Fritsch ; Michael Finke15：
Tricks for Time Series
Forecasting the Economy with Neural Nets: A Survey of Challenges and Solutions / John Moody16：
How to Train Neural Networks / Ralph Neuneier ; Hans Georg Zimmermann17：
Author Index
Subject Index
Introduction
Speeding Learning
Preface
5.

図書
図書
5. Cellular neural networks and analog VLSI : a special issue of analog integrated circuits and signal processing, an international journal, volume 15, no.3 (1998)
edited by Leon O. Chua ... [et al.]
出版情報: Boston : Kluwer Academic Publishers, c1998  103 p. ; 27 cm
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Guest Editorial / L. Chua ; E. Pierzchala ; G. Gulak ; A. Rodriguez-Vazquez
A 16 x 16 Cellular Neural Network Universal Chip: The First Complete Single-Chip Dynamic Computer Array with Distributed Memory and with Gray-Scale Input-Output / J. M. Cruz ; L. O. Chua
A 6 x 6 Cells Interconnection-Oriented Programmable Chip for CNN / M. Salerno ; F. Sargeni ; Vincenzo Bonaiuto
Analog VLSI Design Constraints of Programmable Cellular Neural Networks / P. Kinget ; M. Steyaert
Focal-Plane and Multiple Chip VLSI Approaches to CNNs / M. Anguita ; F. J. Pelayo ; E. Ros ; D. Palomar ; A. Prieto
Architecture and Design of 1-D Enhanced Cellular Neural Network Processors for Signal Detection / M. Y. Wang ; B. J. Sheu ; T. W. Berger ; W. C. Young ; A. K. Cho
Analog VLSI Circuits for Competitive Learning Networks / H. C. Card ; D. K. McNeill ; C. R. Schneider
Design of Neural Networks Based on Wave-Parallel Computing Technique / Y. Yuminaka ; Y. Sasaki ; T. Aoki ; T. Higuchi
Guest Editorial / L. Chua ; E. Pierzchala ; G. Gulak ; A. Rodriguez-Vazquez
A 16 x 16 Cellular Neural Network Universal Chip: The First Complete Single-Chip Dynamic Computer Array with Distributed Memory and with Gray-Scale Input-Output / J. M. Cruz ; L. O. Chua
A 6 x 6 Cells Interconnection-Oriented Programmable Chip for CNN / M. Salerno ; F. Sargeni ; Vincenzo Bonaiuto
6.

図書
図書
6. Independent component analysis : theory and applications (: hardcover)
by Te-Won Lee
出版情報: Boston : Kluwer Academic Publishers, c1998  xxxiii, 210 p. ; 24 cm
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目次情報: 続きを見る
Abstract
Preface
Acknowledgments
List of Figures
List of Tables
Abbreviations and Symbols
Introduction
Independent Component Analysis: Theory / Part I：
Basics / 1：
Independent Component Analysis / 2：
A Unifying Information-Theoretic Framework for ICA / 3：
Blind Separation of Time-Delayed and Convolved Sources / 4：
ICA Using Overcomplete Representations / 5：
First Steps towards Nonlinear ICA / 6：
Independent Component Analysis: Applications / Part II：
Biomedical Applications of ICA / 7：
ICA for Feature Extraction / 8：
Unsupervised Classification with ICA Mixture Models / 9：
Conclusions and Future Research / 10：
Bibliography
About the Author
Index
Abstract
Preface
Acknowledgments
7.

図書
図書
7. 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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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：
8.

図書
図書
8. 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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