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1.

図書

図書
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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2.

図書

図書
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
3.

図書

図書
Witold Pedrycz
出版情報: Boca Raton, Fla. : CRC Press, c1998  284 p. ; 26 cm
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4.

図書

図書
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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5.

図書

図書
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
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:
7.

図書

図書
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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