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Preface Acknowledgment Chapter 1—Introduction 1.1 Pattern Recognition Systems 1.2 Motivation For Artificial Neural Network Approach 1.3 A Prelude To Pattern Recognition 1.4 Statistical Pattern Recognition 1.5 Syntactic Pattern Recognition 1.6 The Character Recognition Problem 1.7 Organization Of Topics References And Bibliography Chapter 2—Neural Networks: An Overview 2.1 Motivation for Overviewing Biological Neural Networks 2.2 Background 2.3 Biological Neural Networks 2.4 Hierarchical Organization in the Brain 2.5 Historical Background 2.6 Artificial Neural Networks References and Bibliography Chapter 3—Preprocessing 3.1 General 3.2 Dealing with Input from a Scanned Image 3.3 Image Compression 3.3.1 Image Compression Example 3.4 Edge Detection 3.5 Skeletonizing 3.5.1 Thinning Example 3.6 Dealing with Input From a Tablet 3.7 Segmentation References and Bibliography Chapter 4—Feed-Forward Networks with Supervised Learning 4.1 Feed-Forward Multilayer Perceptron (FFMLP) Architecture 4.2 FFMLP in C++ 4.3 Training with Back Propagation 4.3.1 Back Propagation in C++ 4.4 A Primitive Example 4.5 Training Strategies and Avoiding Local Minima 4.6 Variations on Gradient Descent 4.6.1 Block Adaptive vs. Data Adaptive Gradient Descent 4.6.2 First-Order vs. Second-Order Gradient Descent 4.7 Topology 4.8 ACON vs. OCON 4.9 Overtraining and Generalization 4.10 Training Set Size and Network Size 4.11 Conjugate Gradient Method 4.12 ALOPEX References and Bibliography Chapter 5—Some Other Types of Neural Networks 5.1 General 5.2 Radial Basis Function Networks 5.2.1 Network Architecture 5.2.2 RBF Training 5.2.3 Applications of RBF Networks 5.3 Higher Order Neural Networks 5.3.1 Introduction 5.3.2 Architecture 5.3.3 Invariance to Geometric Transformations 5.3.4 An Example 5.3.5 Practical Applications References and Bibliography Chapter 6—Feature Extraction I: Geometric Features and Transformations 6.1 General 6.2 Geometric Features (Loops, Intersections, and Endpoints) 6.2.1 Intersections and Endpoints 6.2.2 Loops 6.3 Feature Maps 6.4 A Network Example Using Geometric Features 6.5 Feature Extraction Using Transformations 6.6 Fourier Descriptors 6.7 Gabor Transformations and Wavelets References And Bibliography Chapter 7—Feature Extraction II: Principal Component Analysis 7.1 Dimensionality Reduction 7.2 Principal Components 7.2.1 PCA Example 7.3 Karhunen-Loeve (K-L) Transformation 7.3.1 K-L Transformation Example 7.4 Principal Component Neural Networks 7.5 Applications References and Bibliography Chapter 8—Kohonen Networks and Learning Vector Quantization 8.1 General 8.2 The K-Means Algorithm 8.2.1 K-Means Example 8.3 An Introduction To The Kohonen Model 8.3.1 Kohonen Example 8.4 The Role Of Lateral Feedback 8.5 Kohonen Self-Organizing Feature Map 8.5.1 SOFM Example 8.6 Learning Vector Quantization 8.6.1 LVQ Example 8.7 Variations On LVQ 8.7.1 LVQ2 8.7.2 LVQ2.1 8.7.3 LVQ3 8.7.4 A Final Variation Of LVQ References And Bibliography Chapter 9—Neural Associative Memories and Hopfield Networks 9.1 General 9.2 Linear Associative Memory (LAM) 9.2.1 An Autoassociative LAM Example 9.3 Hopfield Networks 9.4 A Hopfield Example 9.5 Discussion 9.6 Bit Map Example 9.7 Bam Networks 9.8 A Bam Example References And Bibliography Chapter 10—Adaptive Resonance Theory (ART) 10.1 General 10.2 Discovering The Cluster Structure 10.3 Vector Quantization 10.3.1 VQ Example 1 10.3.2 VQ Example 2 10.3.3 VQ Example 3 10.4 Art Philosophy 10.5 The Stability-Plasticity Dilemma 10.6 ART1: Basic Operation 10.7 ART1: Algorithm 10.8 The Gain Control Mechanism 10.8.1 Gain Ccontrol Example 1 10.8.2 Gain Control Example 2 10.9 ART2 Model 10.10 Discussion 10.11 Applications References and Bibliography Chapter 11—Neocognitron 11.1 Introduction 11.2 Architecture 11.3 Example of a System with Sample Training Patterns References and Bibliography Chapter 12—Systems with Multiple Classifiers 12.1 General 12.2 A Framework for Combining Multiple Recognizers 12.3 Voting Schemes 12.4 The Confusion Matrix 12.5 Reliability 12.6 Some Empirical Approaches References and Bibliography Index

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Pattern Recognition with Neural Networks in C++ （825个子文件）

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<HTML> <HEAD> <META name=vsisbn content="0849394627"> <META name=vstitle content="Pattern Recognition with Neural Networks in C++"> <META name=vsauthor content="Abhijit S. Pandya; Robert B. Macy"> <META name=vsimprint content="CRC Press"> <META name=vspublisher content="CRC Press LLC"> <META name=vspubdate content="10/01/95"> <META name=vscategory content="Web and Software Development: Artificial Intelligence: Other,Web and Software Development: Programming, Scripting, and Markup Languages: C/C++,Web and Software Development: Software Engineering: Patterns"> <TITLE>Pattern Recognition with Neural Networks in C++:Index</TITLE>  <STYLE type="text/css">  </STYLE> <META NAME="ROBOTS" CONTENT="NOINDEX, NOFOLLOW">           <CENTER> <TABLE BORDER> <TR> <TD><A HREF="ewtoc.html">Table of Contents</A></TD> </TR> </TABLE> </CENTER> <H2><A NAME="Heading1"></A>Index</H2> A <DL> <DD>Accuracy, 20, 106, 388, 400 <DD>ACON (all classes one network), 119-121 <DD>Activation functions, 35, 73, 75, 154, 319 <DD>Activity bubble, 243 <DD>Adaptive linear combiner (ADALINE), 7-8, 38 <DD>Adaptive principal component extractions (APEX), 206 <DD>Adaptive resonance theory (ART), 41, 43, 189, 321-371 <DD>AdaptWeights, 98 <DD>Address addressable memories, 282 <DD>ALOPEX (algorithm for pattern extraction), 115, 128-144, 186, 205 <DD>Alveolate structure, 305 <DD>AND gate, 35-36 <DD>ANNS, 43 <DD>Antisymmetric activation function, 75 <DD>APEX, 206 <DD>ApplyVector, 80, 109, 113 <DD>Architecture <DL> <DD>ART, 321, 338, 366-367 <DD>ART1, 340-341 <DD>ART2, 364, 366 <DD>bidirectional associative memory, 313 <DD>geometric features, 181 <DD>higher order neural networks, 154 <DD>Hopfield networks, 294-295 <DD>Kohonen model, 231-232 <DD>learning vector quantization, 259-260 <DD>linear associative memory, 283 <DD>neocognitron, 373-380 </DL> <DD>ART, 41, 43, 189, 321-371. See also ART1 network; ART2 network <DD>Artificial intelligence, 6, 39-40. See also Decision-making <DD>Artificial neural networks. See Neural networks <DD>ART1 network, 340-364 <DD>ART2 network, 340, 364-366 <DD>Associatron, 40. See also Memory Asynchronous systems, 4, 294 <DD>Attention systems and subsystems, 340, 364, 381 <DD>Attractors, 304 <DD>Auditory pathway, 33 <DD>Autoassociative memories, 282 <DD>Autoassociative network, 205-206, 282 <DD>Axons, 26-27, 37 </DL> B <DL> <DD>BackProp, 105 <DD>Back propagation, 73, 87-107, 205 <DL> <DD>example, 107-115 <DD>limitations, 129-130 </DL> <DD>BAM, 282, 312-317 <DD>Bayes classifiers, 14, 391 <DD>Bidirectional associative memory (BAM), 282, 312-317 <DD>Biological systems, v, 5-8, 29 <DL> <DD>echo-location in bats, 23 <DD>hearing, 31, 33 <DD>movement control, 25-26, 32-34 <DD>visual perception, 23, 31-32, 187, 240, 373 <DD>vs. neural networks, 24, 25-34, 36, 338 </DL> <DD>Bit map format (BMP), 47-48, 107-108, 187 <DL> <DD>Hopfield networks, 302-312 </DL> <DD>Black box concept, vi <DD>Block adaptive technique, 117-118 <DD>BMP format. See Bit map format (BMP) <DD>Boolean entities, 2 <DD>Bottom-up weights, 345 <DD>Boundaries, 184-186. See also Edge detection <DL> <DD>stroke, 71 </DL> <DD>Brain <DL> <DD>cerebral cortex, 30-31 <DD>hierarchical organization, 28-34 <DD>structure, 25-28 </DL> </DL> C <DL> <DD>Calc methods, 80 <DD>CAM, 282 <DD>C-cells, 373-374, 379-380, 385 <DD>CCIT4 compression algorithm, 48 <DD>Central processing unit (CPU), 42 <DD>CHARACTER GRID MAP, 113-114 <DD>Character recognition, 5, 18-20. See also Handwritten characters; Statistical pattern recognition <DL> <DD>back propagation example, 107-115 <DD>thinning (skeletonizing), 59-66, 166, 179 </DL> <DD>Chromosome structure, 5, 16-17, 149. See also Statistical pattern recognition <DD>C++ implementations <DL> <DD>ALOPEX, 134-144 <DD>ART1, 343-344, 349-355 <DD>back propagation, 96-107 <DD>FFMLP, 77-87 <DD>Hopfield networks, 305-312 <DD>intersections and endpoints, 166-169 <DD>K-means algorithm, 217-230 <DD>Kohonen model, 233-239 <DD>Kohonen SOFM, 243-259 <DD>linear associative memory, 290-293 <DD>loop detection, 172-180 <DD>LVQ, 261-267 <DD>neuron output by two-thirds rule, 344 <DD>vector quantization, 324-328 <DD>voting schemes, 393-395 </DL> <DD>Class-conditional density, 14-15 <DD>Classifiers, 2-4 <DL> <DD>ART1, 340 <DD>class-conditional density, 14-15 <DD>discriminant function generators, 12-13 <DD>distance, 71 <DD>MAXNET, 120-121, 231, 341 <DD>multiple, 387-402 <DD>overlapping, 73 </DL> <DD>C-layer, 180 <DD>Clustering, 15, 149. See also K-means algorithm; K-nearest-neighbor algorithm <DL> <DD>ART, 319-320, 335-336 <DD>ART2, 364 <DD>for characters, 71 <DD>cluster center, 215-216, 224-230, 240, 258-259, 323 <DD>feature, 195 <DD>membership, 230 <DD>population, 216 <DD>radial basis function, 149-151 </DL> <DD>Codebook vector, 259 <DD>Cognitron, 41 <DD>Color palette, 48 <DD>Comparison layer, 340, 342, 345-346 <DD>Competitive learning. See Learning, unsupervised <DD>Compression, of images, 48-52 <DD>Conceptual recognition, 6 <DD>Confusion matrices, 106, 166, 396-400 <DD>Conjugate gradient method, 127-129 <DD>Connectionist paradigm. See Neural networks <DD>Connections, 41, 373, 375-377 <DD>Content addressable memories (CAM), 281-282, 304 <DD>Context processor, 2 <DD>Convergence <DL> <DD>ALOPEX vs. back propagation, 131 <DD>bidirectional associative memory, 312, 316 <DD>criteria, 98 <DD>Hopfield networks, 295, 311-312 <DD>Kohonen SOFM, 247 <DD>test of, 121-122 </DL> <DD>Covariance matrix, 197-198 <DD>CPU, 42 <DD>CThinner, 166-169, 172-180 <DD>Curse of dimensionality, 126 <DD>Cybernetics, historical background, 36 </DL> D <DL> <DD>Data adaptive gradient descent, 117-118 <DD>Data space. See Pattern space <DD>Decision-making function, 3, 6, 12, 15 <DD>Decision space, 389 <DD>Decision-theoretical approach, 11-12 <DD>Delta-bar-delta algorithm, 116-117 <DD>Dendric subunits, 29 <DD>Dendrites, 26-27 <DD>Destination neurons, 88, 90-91 <DD>DFG, 12-14 <DD>Dimensionality, 126 <DL> <DD>in radial basis functions, 147 <DD>reduction, 165, 195-197, 204 </DL> <DD>Discriminant function generators (DFGs), 12-14 <DD>Discriminant functions, 11 <DD>Distance threshold, 323, 334 </DL> E <DL> <DD>Edge detection, 53-59. See also Boundaries <DD>EDIT, 113-114 <DD>EDVAC computer, 36 <DD>Eigenvector, 196, 199-200, 202-203 <DL> <DD>computation by neural networks, 205-206 </DL> <DD>Elementary particle collision photographs,5, 149. See also Statistical pattern recognition <DD>Endpoints, 166 <DD>Energy flow cubes, 294, 296 <DD>Epochs, 96, 122 <DL> <DD>Kohonen model, 238-239 <DD>SOFM, 254, 256-257 </DL> <DD>Equilibrium state, 242 <DD>Error convergence, 133 <DD>Error correction. See Learning <DD>Error measure, 87, 96 <DL> <DD>excessively low, 121-126 <DD>Fourier descriptor, 186 <DD>high, 115 </DL> <DD>Error signal, 89 <DD>Error tolerance, 121 <DD>Euclidean function, 246 <DD>Euclidean norm distance, 232 <DD>Exemplar pattern vector, 276, 28

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