Self-organizing Map Formation: Foundations of Neural ComputationKlaus Obermayer, Terrence Joseph Sejnowski MIT Press, 2001 - 440 lappuses This book provides an overview of self-organizing map formation, including recent developments. Self-organizing maps form a branch of unsupervised learning, which is the study of what can be determined about the statistical properties of input data without explicit feedback from a teacher. The articles are drawn from the journal Neural Computation.The book consists of five sections. The first section looks at attempts to model the organization of cortical maps and at the theory and applications of the related artificial neural network algorithms. The second section analyzes topographic maps and their formation via objective functions. The third section discusses cortical maps of stimulus features. The fourth section discusses self-organizing maps for unsupervised data analysis. The fifth section discusses extensions of self-organizing maps, including two surprising applications of mapping algorithms to standard computer science problems: combinatorial optimization and sorting. Contributors |
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1.–5. rezultāts no 73.
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Saturs
Analysis of Linskers Simulations of Hebbian Rules | 3 |
Toward a Theory of the Striate Cortex | 19 |
Bayesian SelfOrganization Driven by Prior Probability Distributions | 39 |
Dynamics and Formation of SelfOrganizing Maps | 55 |
A Unifying Objective Function for Topographic Mappings | 69 |
A Unifying | 83 |
How to Generate Ordered Maps by Maximizing the Mutual | 129 |
Models of Orientation and Ocular Dominance Columns in | 141 |
Hyperparameter Selection for SelfOrganizing Maps | 277 |
The Generative Topographic Mapping | 291 |
SelfOrganization as an Iterative Kernel Smoothing Process | 311 |
A Stochastic SelfOrganizing Map for Proximity Data | 327 |
SelfOrganized Formation of Various InvariantFeature Filters | 345 |
Faithful Representation of Separable Distributions | 369 |
Dynamic Cell Structure Learns Perfectly Topology Preserving Map | 385 |
An Analysis of the Elastic Net Approach to the Traveling | 407 |
Development of Oriented Ocular Dominance Bands as | 185 |
A SelfOrganizing Model of Color Blob Formation | 213 |
A Type of Duality between SelfOrganizing Maps and Minimal | 235 |
A Bayesian Analysis of SelfOrganizing Maps | 249 |
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426 | |
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Atsauces uz šo grāmatu
Unsupervised Learning: Foundations of Neural Computation Geoffrey Hinton,Terrence J. Sejnowski Ierobežota priekšskatīšana - 1999 |