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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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目次情報: 続きを見る
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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