{"product_id":"classification-analysis-of-dna-microarrays-multiple-component-retail-product-parts-enclosed-9780470170816","title":"Classification Analysis of DNA Microarrays (Multiple-component retail product, part(s) enclosed) 9780470170816","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eClassification Analysis of DNA Microarrays\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cfont size=\"4\"\u003eLeif E. Peterson (Author), Yi Pan (Series edited by), Albert Y. Zomaya (Series edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780470170816, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eMultiple-component retail product, part(s) enclosed, published 17 May 2013\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e736 pages\u003cbr\u003e23.4 x 15.5 x 4.1 cm, 1.157 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003cb\u003eWiley Series in Bioinformatics: Computational Techniques and Engineering\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eYi Pan and Albert Y. Zomaya, Series Editors\u003c\/i\u003e \u003c\/p\u003e\n\u003cp\u003e\u003cb\u003eWide coverage of traditional unsupervised and supervised methods and newer contemporary approaches that help researchers handle the rapid growth of classification methods in DNA microarray studies\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003eProliferating classification methods in DNA microarray studies have resulted in a body of information scattered throughout literature, conference proceedings, and elsewhere. This book unites many of these classification methods in a single volume. In addition to traditional statistical methods, it covers newer machine-learning approaches such as fuzzy methods, artificial neural networks, evolutionary-based genetic algorithms, support vector machines, swarm intelligence involving particle swarm optimization, and more. \u003c\/p\u003e\n\u003cp\u003e\u003ci\u003eClassification Analysis of DNA Microarrays\u003c\/i\u003e provides highly detailed pseudo-code and rich, graphical programming features, plus ready-to-run source code. Along with primary methods that include traditional and contemporary classification, it offers supplementary tools and data preparation routines for standardization and fuzzification; dimensional reduction via crisp and fuzzy c-means, PCA, and non-linear manifold learning; and computational linguistics via text analytics and n-gram analysis, recursive feature extraction during ANN, kernel-based methods, ensemble classifier fusion. \u003c\/p\u003e\n\u003cp\u003eThis powerful new resource: \u003c\/p\u003e\n\u003cul\u003e \u003cli\u003eProvides information on the use of classification analysis for DNA microarrays used for large-scale high-throughput transcriptional studies\u003c\/li\u003e \u003cli\u003eServes as a historical repository of general use supervised classification methods as well as newer contemporary methods\u003c\/li\u003e \u003cli\u003eBrings the reader quickly up to speed on the various classification methods by implementing the programming pseudo-code and source code provided in the book\u003c\/li\u003e \u003cli\u003eDescribes implementation methods that help shorten discovery times\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eClassification Analysis of DNA Microarrays is useful for professionals and graduate students in computer science, bioinformatics, biostatistics, systems biology, and many related fields.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003ePreface xix\u003c\/p\u003e \u003cp\u003eAbbreviations xxiii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Class Discovery 2\u003c\/p\u003e \u003cp\u003e1.2 Dimensional Reduction 4\u003c\/p\u003e \u003cp\u003e1.3 Class Prediction 4\u003c\/p\u003e \u003cp\u003e1.4 Classification Rules of Thumb 5\u003c\/p\u003e \u003cp\u003e1.5 DNA Microarray Datasets Used 9\u003c\/p\u003e \u003cp\u003eReferences 11\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I Class Discovery 13\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Crisp \u003ci\u003eK\u003c\/i\u003e-Means Cluster Analysis 15\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 15\u003c\/p\u003e \u003cp\u003e2.2 Algorithm 16\u003c\/p\u003e \u003cp\u003e2.3 Implementation 18\u003c\/p\u003e \u003cp\u003e2.4 Distance Metrics 20\u003c\/p\u003e \u003cp\u003e2.5 Cluster Validity 24\u003c\/p\u003e \u003cp\u003e2.5.1 Davies–Bouldin Index 25\u003c\/p\u003e \u003cp\u003e2.5.2 Dunn’s Index 25\u003c\/p\u003e \u003cp\u003e2.5.3 Intracluster Distance 26\u003c\/p\u003e \u003cp\u003e2.5.4 Intercluster Distance 27\u003c\/p\u003e \u003cp\u003e2.5.5 Silhouette Index 30\u003c\/p\u003e \u003cp\u003e2.5.6 Hubert’s Statistic 31\u003c\/p\u003e \u003cp\u003e2.5.7 Randomization Tests for Optimal Value of \u003ci\u003eK \u003c\/i\u003e31\u003c\/p\u003e \u003cp\u003e2.6 \u003ci\u003eV\u003c\/i\u003e-Fold Cross-Validation 35\u003c\/p\u003e \u003cp\u003e2.7 Cluster Initialization 37\u003c\/p\u003e \u003cp\u003e2.7.1 \u003ci\u003eK \u003c\/i\u003eRandomly Selected Microarrays 37\u003c\/p\u003e \u003cp\u003e2.7.2 \u003ci\u003eK \u003c\/i\u003eRandom Partitions 40\u003c\/p\u003e \u003cp\u003e2.7.3 Prototype Splitting 41\u003c\/p\u003e \u003cp\u003e2.8 Cluster Outliers 44\u003c\/p\u003e \u003cp\u003e2.9 Summary 44\u003c\/p\u003e \u003cp\u003eReferences 45\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Fuzzy \u003ci\u003eK\u003c\/i\u003e-Means Cluster Analysis 47\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 47\u003c\/p\u003e \u003cp\u003e3.2 Fuzzy \u003ci\u003eK\u003c\/i\u003e-Means Algorithm 47\u003c\/p\u003e \u003cp\u003e3.3 Implementation 49\u003c\/p\u003e \u003cp\u003e3.4 Summary 54\u003c\/p\u003e \u003cp\u003eReferences 54\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Self-Organizing Maps 57\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 57\u003c\/p\u003e \u003cp\u003e4.2 Algorithm 57\u003c\/p\u003e \u003cp\u003e4.2.1 Feature Transformation and Reference Vector Initialization 59\u003c\/p\u003e \u003cp\u003e4.2.2 Learning 60\u003c\/p\u003e \u003cp\u003e4.2.3 Conscience 61\u003c\/p\u003e \u003cp\u003e4.3 Implementation 63\u003c\/p\u003e \u003cp\u003e4.3.1 Feature Transformation and Reference Vector Initialization 63\u003c\/p\u003e \u003cp\u003e4.3.2 Reference Vector Weight Learning 66\u003c\/p\u003e \u003cp\u003e4.4 Cluster Visualization 67\u003c\/p\u003e \u003cp\u003e4.4.1 Crisp \u003ci\u003eK\u003c\/i\u003e-Means Cluster Analysis 67\u003c\/p\u003e \u003cp\u003e4.4.2 Adjacency Matrix Method 68\u003c\/p\u003e \u003cp\u003e4.4.3 Cluster Connectivity Method 69\u003c\/p\u003e \u003cp\u003e4.4.4 Hue–Saturation–Value (HSV) Color Normalization 69\u003c\/p\u003e \u003cp\u003e4.5 Unified Distance Matrix (\u003ci\u003eU \u003c\/i\u003eMatrix) 71\u003c\/p\u003e \u003cp\u003e4.6 Component Map 71\u003c\/p\u003e \u003cp\u003e4.7 Map Quality 73\u003c\/p\u003e \u003cp\u003e4.8 Nonlinear Dimension Reduction 75\u003c\/p\u003e \u003cp\u003eReferences 79\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Unsupervised Neural Gas 81\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 81\u003c\/p\u003e \u003cp\u003e5.2 Algorithm 82\u003c\/p\u003e \u003cp\u003e5.3 Implementation 82\u003c\/p\u003e \u003cp\u003e5.3.1 Feature Transformation and Prototype Initialization 82\u003c\/p\u003e \u003cp\u003e5.3.2 Prototype Learning 83\u003c\/p\u003e \u003cp\u003e5.4 Nonlinear Dimension Reduction 85\u003c\/p\u003e \u003cp\u003e5.5 Summary 87\u003c\/p\u003e \u003cp\u003eReferences 88\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Hierarchical Cluster Analysis 91\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 91\u003c\/p\u003e \u003cp\u003e6.2 Methods 91\u003c\/p\u003e \u003cp\u003e6.2.1 General Programming Methods 91\u003c\/p\u003e \u003cp\u003e6.2.2 Step 1: Cluster-Analyzing Arrays as Objects with Genes as Attributes 92\u003c\/p\u003e \u003cp\u003e6.2.3 Step 2: Cluster-Analyzing Genes as Objects with Arrays as Attributes 94\u003c\/p\u003e \u003cp\u003e6.3 Algorithm 96\u003c\/p\u003e \u003cp\u003e6.4 Implementation 96\u003c\/p\u003e \u003cp\u003e6.4.1 Heatmap Color Control 96\u003c\/p\u003e \u003cp\u003e6.4.2 User Choices for Clustering Arrays and Genes 97\u003c\/p\u003e \u003cp\u003e6.4.3 Distance Matrices and Agglomeration Sequences 98\u003c\/p\u003e \u003cp\u003e6.4.4 Drawing Dendograms and Heatmaps 104\u003c\/p\u003e \u003cp\u003eReferences 105\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Model-Based Clustering 107\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 107\u003c\/p\u003e \u003cp\u003e7.2 Algorithm 110\u003c\/p\u003e \u003cp\u003e7.3 Implementation 111\u003c\/p\u003e \u003cp\u003e7.4 Summary 116\u003c\/p\u003e \u003cp\u003eReferences 117\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Text Mining: Document Clustering 119\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 119\u003c\/p\u003e \u003cp\u003e8.2 Duo-Mining 119\u003c\/p\u003e \u003cp\u003e8.3 Streams and Documents 120\u003c\/p\u003e \u003cp\u003e8.4 Lexical Analysis 120\u003c\/p\u003e \u003cp\u003e8.4.1 Automatic Indexing 120\u003c\/p\u003e \u003cp\u003e8.4.2 Removing Stopwords 121\u003c\/p\u003e \u003cp\u003e8.5 Stemming 121\u003c\/p\u003e \u003cp\u003e8.6 Term Weighting 121\u003c\/p\u003e \u003cp\u003e8.7 Concept Vectors 124\u003c\/p\u003e \u003cp\u003e8.8 Main Terms Representing Concept Vectors 124\u003c\/p\u003e \u003cp\u003e8.9 Algorithm 125\u003c\/p\u003e \u003cp\u003e8.10 Preprocessing 127\u003c\/p\u003e \u003cp\u003e8.11 Summary 137\u003c\/p\u003e \u003cp\u003eReferences 137\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Text Mining: \u003ci\u003eN\u003c\/i\u003e-Gram Analysis 139\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 139\u003c\/p\u003e \u003cp\u003e9.2 Algorithm 140\u003c\/p\u003e \u003cp\u003e9.3 Implementation 141\u003c\/p\u003e \u003cp\u003e9.4 Summary 154\u003c\/p\u003e \u003cp\u003eReferences 156\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II Dimension Reduction 159\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Principal Components Analysis 161\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 161\u003c\/p\u003e \u003cp\u003e10.2 Multivariate Statistical Theory 161\u003c\/p\u003e \u003cp\u003e10.2.1 Matrix Definitions 162\u003c\/p\u003e \u003cp\u003e10.2.2 Principal Component Solution of R 163\u003c\/p\u003e \u003cp\u003e10.2.3 Extraction of Principal Components 164\u003c\/p\u003e \u003cp\u003e10.2.4 Varimax Orthogonal Rotation of Components 166\u003c\/p\u003e \u003cp\u003e10.2.5 Principal Component Score Coefficients 168\u003c\/p\u003e \u003cp\u003e10.2.6 Principal Component Scores 169\u003c\/p\u003e \u003cp\u003e10.3 Algorithm 170\u003c\/p\u003e \u003cp\u003e10.4 When to Use Loadings and PC Scores 170\u003c\/p\u003e \u003cp\u003e10.5 Implementation 171\u003c\/p\u003e \u003cp\u003e10.5.1 Correlation Matrix R 171\u003c\/p\u003e \u003cp\u003e10.5.2 Eigenanalysis of Correlation Matrix R 172\u003c\/p\u003e \u003cp\u003e10.5.3 Determination of Loadings and Varimax Rotation 174\u003c\/p\u003e \u003cp\u003e10.5.4 Calculating Principal Component (PC) Scores 176\u003c\/p\u003e \u003cp\u003e10.6 Rules of Thumb For PCA 182\u003c\/p\u003e \u003cp\u003e10.7 Summary 186\u003c\/p\u003e \u003cp\u003eReferences 187\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Nonlinear Manifold Learning 189\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 189\u003c\/p\u003e \u003cp\u003e11.2 Correlation-Based PCA 190\u003c\/p\u003e \u003cp\u003e11.3 Kernel PCA 191\u003c\/p\u003e \u003cp\u003e11.4 Diffusion Maps 192\u003c\/p\u003e \u003cp\u003e11.5 Laplacian Eigenmaps 192\u003c\/p\u003e \u003cp\u003e11.6 Local Linear Embedding 193\u003c\/p\u003e \u003cp\u003e11.7 Locality Preserving Projections 194\u003c\/p\u003e \u003cp\u003e11.8 Sammon Mapping 195\u003c\/p\u003e \u003cp\u003e11.9 NLML Prior to Classification Analysis 195\u003c\/p\u003e \u003cp\u003e11.10 Classification Results 197\u003c\/p\u003e \u003cp\u003e11.11 Summary 200\u003c\/p\u003e \u003cp\u003eReferences 203\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III Class Prediction 205\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Feature Selection 207\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 207\u003c\/p\u003e \u003cp\u003e12.2 Filtering versus Wrapping 208\u003c\/p\u003e \u003cp\u003e12.3 Data 209\u003c\/p\u003e \u003cp\u003e12.3.1 Numbers 209\u003c\/p\u003e \u003cp\u003e12.3.2 Responses 209\u003c\/p\u003e \u003cp\u003e12.3.3 Measurement Scales 210\u003c\/p\u003e \u003cp\u003e12.3.4 Variables 211\u003c\/p\u003e \u003cp\u003e12.4 Data Arrangement 211\u003c\/p\u003e \u003cp\u003e12.5 Filtering 213\u003c\/p\u003e \u003cp\u003e12.5.1 Continuous Features 213\u003c\/p\u003e \u003cp\u003e12.5.2 Best Rank Filters 219\u003c\/p\u003e \u003cp\u003e12.5.3 Randomization Tests 236\u003c\/p\u003e \u003cp\u003e12.5.4 Multitesting Problem 237\u003c\/p\u003e \u003cp\u003e12.5.5 Filtering Qualitative Features 242\u003c\/p\u003e \u003cp\u003e12.5.6 Multiclass Gini Diversity Index 246\u003c\/p\u003e \u003cp\u003e12.5.7 Class Comparison Techniques 247\u003c\/p\u003e \u003cp\u003e12.5.8 Generation of Nonredundant Gene List 250\u003c\/p\u003e \u003cp\u003e12.6 Selection Methods 254\u003c\/p\u003e \u003cp\u003e12.6.1 Greedy Plus Takeaway (Greedy PTA) 254\u003c\/p\u003e \u003cp\u003e12.6.2 Best Ranked Genes 258\u003c\/p\u003e \u003cp\u003e12.7 Multicollinearity 259\u003c\/p\u003e \u003cp\u003e12.8 Summary 270\u003c\/p\u003e \u003cp\u003eReferences 270\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Classifier Performance 273\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 273\u003c\/p\u003e \u003cp\u003e13.2 Input–Output, Speed, and Efficiency 273\u003c\/p\u003e \u003cp\u003e13.3 Training, Testing, and Validation 277\u003c\/p\u003e \u003cp\u003e13.4 Ensemble Classifier Fusion 280\u003c\/p\u003e \u003cp\u003e13.5 Sensitivity and Specificity 283\u003c\/p\u003e \u003cp\u003e13.6 Bias 284\u003c\/p\u003e \u003cp\u003e13.7 Variance 285\u003c\/p\u003e \u003cp\u003e13.8 Receiver–Operator Characteristic (ROC) Curves 286\u003c\/p\u003e \u003cp\u003eReferences 295\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Linear Regression 297\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 297\u003c\/p\u003e \u003cp\u003e14.2 Algorithm 299\u003c\/p\u003e \u003cp\u003e14.3 Implementation 299\u003c\/p\u003e \u003cp\u003e14.4 Cross-Validation Results 300\u003c\/p\u003e \u003cp\u003e14.5 Bootstrap Bias 303\u003c\/p\u003e \u003cp\u003e14.6 Multiclass ROC Curves 306\u003c\/p\u003e \u003cp\u003e14.7 Decision Boundaries 308\u003c\/p\u003e \u003cp\u003e14.8 Summary 310\u003c\/p\u003e \u003cp\u003eReferences 310\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Decision Tree Classification 311\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 311\u003c\/p\u003e \u003cp\u003e15.2 Features Used 314\u003c\/p\u003e \u003cp\u003e15.3 Terminal Nodes and Stopping Criteria 315\u003c\/p\u003e \u003cp\u003e15.4 Algorithm 315\u003c\/p\u003e \u003cp\u003e15.5 Implementation 315\u003c\/p\u003e \u003cp\u003e15.6 Cross-Validation Results 318\u003c\/p\u003e \u003cp\u003e15.7 Decision Boundaries 326\u003c\/p\u003e \u003cp\u003e15.8 Summary 327\u003c\/p\u003e \u003cp\u003eReferences 329\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Random Forests 331\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 331\u003c\/p\u003e \u003cp\u003e16.2 Algorithm 333\u003c\/p\u003e \u003cp\u003e16.3 Importance Scores 334\u003c\/p\u003e \u003cp\u003e16.4 Strength and Correlation 338\u003c\/p\u003e \u003cp\u003e16.5 Proximity and Supervised Clustering 342\u003c\/p\u003e \u003cp\u003e16.6 Unsupervised Clustering 345\u003c\/p\u003e \u003cp\u003e16.7 Class Outlier Detection 348\u003c\/p\u003e \u003cp\u003e16.8 Implementation 350\u003c\/p\u003e \u003cp\u003e16.9 Parameter Effects 350\u003c\/p\u003e \u003cp\u003e16.10 Summary 357\u003c\/p\u003e \u003cp\u003eReferences 358\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 \u003ci\u003eK \u003c\/i\u003eNearest Neighbor 361\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e17.1 Introduction 361\u003c\/p\u003e \u003cp\u003e17.2 Algorithm 362\u003c\/p\u003e \u003cp\u003e17.3 Implementation 363\u003c\/p\u003e \u003cp\u003e17.4 Cross-Validation Results 364\u003c\/p\u003e \u003cp\u003e17.5 Bootstrap Bias 369\u003c\/p\u003e \u003cp\u003e17.6 Multiclass ROC Curves 373\u003c\/p\u003e \u003cp\u003e17.7 Decision Boundaries 374\u003c\/p\u003e \u003cp\u003e17.8 Summary 377\u003c\/p\u003e \u003cp\u003eReferences 378\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 Na\u003c\/b\u003e\u003cb\u003eїve Bayes Classifier 379\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e18.1 Introduction 379\u003c\/p\u003e \u003cp\u003e18.2 Algorithm 380\u003c\/p\u003e \u003cp\u003e18.3 Cross-Validation Results 380\u003c\/p\u003e \u003cp\u003e18.4 Bootstrap Bias 384\u003c\/p\u003e \u003cp\u003e18.5 Multiclass ROC Curves 386\u003c\/p\u003e \u003cp\u003e18.6 Decision Boundaries 386\u003c\/p\u003e \u003cp\u003e18.7 Summary 389\u003c\/p\u003e \u003cp\u003eReferences 391\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 Linear Discriminant Analysis 393\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e19.1 Introduction 393\u003c\/p\u003e \u003cp\u003e19.2 Multivariate Matrix Definitions 394\u003c\/p\u003e \u003cp\u003e19.3 Linear Discriminant Analysis 396\u003c\/p\u003e \u003cp\u003e19.3.1 Algorithm 397\u003c\/p\u003e \u003cp\u003e19.3.2 Cross-Validation Results 397\u003c\/p\u003e \u003cp\u003e19.3.3 Bootstrap Bias 401\u003c\/p\u003e \u003cp\u003e19.3.4 Multiclass ROC Curves 402\u003c\/p\u003e \u003cp\u003e19.3.5 Decision Boundaries 403\u003c\/p\u003e \u003cp\u003e19.4 Quadratic Discriminant Analysis 403\u003c\/p\u003e \u003cp\u003e19.5 Fisher’s Discriminant Analysis 406\u003c\/p\u003e \u003cp\u003e19.6 Summary 411\u003c\/p\u003e \u003cp\u003eReferences 412\u003c\/p\u003e \u003cp\u003e\u003cb\u003e20 Learning Vector Quantization 415\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e20.1 Introduction 415\u003c\/p\u003e \u003cp\u003e20.2 Cross-Validation Results 417\u003c\/p\u003e \u003cp\u003e20.3 Bootstrap Bias 417\u003c\/p\u003e \u003cp\u003e20.4 Multiclass ROC Curves 426\u003c\/p\u003e \u003cp\u003e20.5 Decision Boundaries 428\u003c\/p\u003e \u003cp\u003e20.6 Summary 428\u003c\/p\u003e \u003cp\u003eReferences 430\u003c\/p\u003e \u003cp\u003e\u003cb\u003e21 Logistic Regression 433\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e21.1 Introduction 433\u003c\/p\u003e \u003cp\u003e21.2 Binary Logistic Regression 434\u003c\/p\u003e \u003cp\u003e21.3 Polytomous Logistic Regression 439\u003c\/p\u003e \u003cp\u003e21.4 Cross-Validation Results 443\u003c\/p\u003e \u003cp\u003e21.5 Decision Boundaries 444\u003c\/p\u003e \u003cp\u003e21.6 Summary 444\u003c\/p\u003e \u003cp\u003eReferences 447\u003c\/p\u003e \u003cp\u003e\u003cb\u003e22 Support Vector Machines 449\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e22.1 Introduction 449\u003c\/p\u003e \u003cp\u003e22.2 Hard-Margin SVM for Linearly Separable Classes 449\u003c\/p\u003e \u003cp\u003e22.3 Kernel Mapping into Nonlinear Feature Space 452\u003c\/p\u003e \u003cp\u003e22.4 Soft-Margin SVM for Nonlinearly Separable Classes 452\u003c\/p\u003e \u003cp\u003e22.5 Gradient Ascent Soft-Margin SVM 454\u003c\/p\u003e \u003cp\u003e22.5.1 Cross-Validation Results 455\u003c\/p\u003e \u003cp\u003e22.5.2 Bootstrap Bias 457\u003c\/p\u003e \u003cp\u003e22.5.3 Multiclass ROC Curves 465\u003c\/p\u003e \u003cp\u003e22.5.4 Decision Boundaries 465\u003c\/p\u003e \u003cp\u003e22.6 Least-Squares Soft-Margin SVM 465\u003c\/p\u003e \u003cp\u003e22.6.1 Cross-Validation Results 470\u003c\/p\u003e \u003cp\u003e22.6.2 Bootstrap Bias 477\u003c\/p\u003e \u003cp\u003e22.6.3 Multiclass ROC Curves 477\u003c\/p\u003e \u003cp\u003e22.6.4 Decision Boundaries 477\u003c\/p\u003e \u003cp\u003e22.7 Summary 481\u003c\/p\u003e \u003cp\u003eReferences 483\u003c\/p\u003e \u003cp\u003e\u003cb\u003e23 Artificial Neural Networks 487\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e23.1 Introduction 487\u003c\/p\u003e \u003cp\u003e23.2 ANN Architecture 488\u003c\/p\u003e \u003cp\u003e23.3 Basics of ANN Training 488\u003c\/p\u003e \u003cp\u003e23.3.1 Backpropagation Learning 493\u003c\/p\u003e \u003cp\u003e23.3.2 Resilient Backpropagation (RPROP) Learning 496\u003c\/p\u003e \u003cp\u003e23.3.3 Cycles and Epochs 496\u003c\/p\u003e \u003cp\u003e23.4 ANN Training Methods 497\u003c\/p\u003e \u003cp\u003e23.4.1 Method 1: Gene Dimensional Reduction and Recursive Feature Elimination for Large Gene Lists 497\u003c\/p\u003e \u003cp\u003e23.4.2 Method 2: Gene Filtering and Selection 502\u003c\/p\u003e \u003cp\u003e23.5 Algorithm 502\u003c\/p\u003e \u003cp\u003e23.6 Batch versus Online Training 504\u003c\/p\u003e \u003cp\u003e23.7 ANN Testing 504\u003c\/p\u003e \u003cp\u003e23.8 Cross-Validation Results 504\u003c\/p\u003e \u003cp\u003e23.9 Bootstrap Bias 506\u003c\/p\u003e \u003cp\u003e23.10 Multiclass ROC Curves 506\u003c\/p\u003e \u003cp\u003e23.11 Decision Boundaries 513\u003c\/p\u003e \u003cp\u003e23.12 RPROP versus Backpropagation 513\u003c\/p\u003e \u003cp\u003e23.13 Summary 522\u003c\/p\u003e \u003cp\u003eReferences 522\u003c\/p\u003e \u003cp\u003e\u003cb\u003e24 Kernel Regression 525\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e24.1 Introduction 525\u003c\/p\u003e \u003cp\u003e24.2 Algorithm 527\u003c\/p\u003e \u003cp\u003e24.3 Cross-Validation Results 527\u003c\/p\u003e \u003cp\u003e24.4 Bootstrap Bias 528\u003c\/p\u003e \u003cp\u003e24.5 Multiclass ROC Curves 536\u003c\/p\u003e \u003cp\u003e24.6 Decision Boundaries 537\u003c\/p\u003e \u003cp\u003e24.7 Summary 540\u003c\/p\u003e \u003cp\u003eReferences 542\u003c\/p\u003e \u003cp\u003e\u003cb\u003e25 Neural Adaptive Learning with Metaheuristics 543\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e25.1 Multilayer Perceptrons 544\u003c\/p\u003e \u003cp\u003e25.2 Genetic Algorithms 544\u003c\/p\u003e \u003cp\u003e25.3 Covariance Matrix Self-Adaptation–Evolution Strategies 549\u003c\/p\u003e \u003cp\u003e25.4 Particle Swarm Optimization 556\u003c\/p\u003e \u003cp\u003e25.5 ANT Colony Optimization 560\u003c\/p\u003e \u003cp\u003e25.5.1 Classification 560\u003c\/p\u003e \u003cp\u003e25.5.2 Continuous-Function Approximation 562\u003c\/p\u003e \u003cp\u003e25.6 Summary 567\u003c\/p\u003e \u003cp\u003eReferences 567\u003c\/p\u003e \u003cp\u003e\u003cb\u003e26 Supervised Neural Gas 573\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e26.1 Introduction 573\u003c\/p\u003e \u003cp\u003e26.2 Algorithm 574\u003c\/p\u003e \u003cp\u003e26.3 Cross-Validation Results 574\u003c\/p\u003e \u003cp\u003e26.4 Bootstrap Bias 582\u003c\/p\u003e \u003cp\u003e26.5 Multiclass ROC Curves 582\u003c\/p\u003e \u003cp\u003e26.6 Class Decision Boundaries 584\u003c\/p\u003e \u003cp\u003e26.7 Summary 586\u003c\/p\u003e \u003cp\u003eReferences 588\u003c\/p\u003e \u003cp\u003e\u003cb\u003e27 Mixture of Experts 591\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e27.1 Introduction 591\u003c\/p\u003e \u003cp\u003e27.2 Algorithm 595\u003c\/p\u003e \u003cp\u003e27.3 Cross-Validation Results 596\u003c\/p\u003e \u003cp\u003e27.4 Decision Boundaries 597\u003c\/p\u003e \u003cp\u003e27.5 Summary 597\u003c\/p\u003e \u003cp\u003eReferences 599\u003c\/p\u003e \u003cp\u003e\u003cb\u003e28 Covariance Matrix Filtering 601\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e28.1 Introduction 601\u003c\/p\u003e \u003cp\u003e28.2 Covariance and Correlation Matrices 601\u003c\/p\u003e \u003cp\u003e28.3 Random Matrices 602\u003c\/p\u003e \u003cp\u003e28.4 Component Subtraction 608\u003c\/p\u003e \u003cp\u003e28.5 Covariance Matrix Shrinkage 610\u003c\/p\u003e \u003cp\u003e28.6 Covariance Matrix Filtering 613\u003c\/p\u003e \u003cp\u003e28.7 Summary 621\u003c\/p\u003e \u003cp\u003eReferences 622\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendixes 625\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eA Probability Primer 627\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA.1 Choices 627\u003c\/p\u003e \u003cp\u003eA.2 Permutations 628\u003c\/p\u003e \u003cp\u003eA.3 Combinations 630\u003c\/p\u003e \u003cp\u003eA.4 Probability 632\u003c\/p\u003e \u003cp\u003eA.4.1 Addition Rule 633\u003c\/p\u003e \u003cp\u003eA.4.2 Multiplication Rule and Conditional Probabilities 634\u003c\/p\u003e \u003cp\u003eA.4.3 Multiplication Rule for Independent Events 635\u003c\/p\u003e \u003cp\u003eA.4.4 Elimination Rule (Disease Prevalence) 636\u003c\/p\u003e \u003cp\u003eA.4.5 Bayes’ Rule (Pathway Probabilities) 637\u003c\/p\u003e \u003cp\u003e\u003cb\u003eB Matrix Algebra 639\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eB.1 Vectors 639\u003c\/p\u003e \u003cp\u003eB.2 Matrices 642\u003c\/p\u003e \u003cp\u003eB.3 Sample Mean, Covariance, and Correlation 647\u003c\/p\u003e \u003cp\u003eB.4 Diagonal Matrices 648\u003c\/p\u003e \u003cp\u003eB.5 Identity Matrices 649\u003c\/p\u003e \u003cp\u003eB.6 Trace of a Matrix 650\u003c\/p\u003e \u003cp\u003eB.7 Eigenanalysis 650\u003c\/p\u003e \u003cp\u003eB.8 Symmetric Eigenvalue Problem 650\u003c\/p\u003e \u003cp\u003eB.9 Generalized Eigenvalue Problem 651\u003c\/p\u003e \u003cp\u003eB.10 Matrix Properties 652\u003c\/p\u003e \u003cp\u003e\u003cb\u003eC Mathematical Functions 655\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eC.1 Inequalities 655\u003c\/p\u003e \u003cp\u003eC.2 Laws of Exponents 655\u003c\/p\u003e \u003cp\u003eC.3 Laws of Radicals 656\u003c\/p\u003e \u003cp\u003eC.4 Absolute Value 656\u003c\/p\u003e \u003cp\u003eC.5 Logarithms 656\u003c\/p\u003e \u003cp\u003eC.6 Product and Summation Operators 657\u003c\/p\u003e \u003cp\u003eC.7 Partial Derivatives 657\u003c\/p\u003e \u003cp\u003eC.8 Likelihood Functions 658\u003c\/p\u003e \u003cp\u003e\u003cb\u003eD Statistical Primitives 665\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eD.1 Rules of Thumb 665\u003c\/p\u003e \u003cp\u003eD.2 Primitives 668\u003c\/p\u003e \u003cp\u003eReferences 678\u003c\/p\u003e \u003cp\u003e\u003cb\u003eE Probability Distributions 679\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eE.1 Basics of Hypothesis Testing 679\u003c\/p\u003e \u003cp\u003eE.2 Probability Functions: Source of \u003ci\u003ep \u003c\/i\u003eValues 682\u003c\/p\u003e \u003cp\u003eE.3 Normal Distribution 682\u003c\/p\u003e \u003cp\u003eE.4 Gamma Function 686\u003c\/p\u003e \u003cp\u003eE.5 Beta Function 689\u003c\/p\u003e \u003cp\u003eE.6 Pseudo-Random-Number Generation 692\u003c\/p\u003e \u003cp\u003eE.6.1 Standard Uniform Distribution 692\u003c\/p\u003e \u003cp\u003eE.6.2 Normal Distribution 693\u003c\/p\u003e \u003cp\u003eE.6.3 Lognormal Distribution 694\u003c\/p\u003e \u003cp\u003eE.6.4 Binomial Distribution 695\u003c\/p\u003e \u003cp\u003eE.6.5 Poisson Distribution 696\u003c\/p\u003e \u003cp\u003eE.6.6 Triangle Distribution 697\u003c\/p\u003e \u003cp\u003eE.6.7 Log-Triangle Distribution 698\u003c\/p\u003e \u003cp\u003eReferences 698\u003c\/p\u003e \u003cp\u003e\u003cb\u003eF Symbols and Notation 699\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIndex 703\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Computer networking \u0026amp; communications [\u003ca title=\"See our other books on Computer networking \u0026amp; communications\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Computer%20networking%20\u0026amp;%20communications%20%5BUT%5D%22\"\u003eUT\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley-IEEE Computer Society Pr","offers":[{"title":"Brand New","offer_id":52257148272920,"sku":"9780470170816","price":81.87,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9780470170816.jpg?v=1781277644","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/classification-analysis-of-dna-microarrays-multiple-component-retail-product-parts-enclosed-9780470170816","provider":"Freshly Printed Books","version":"1.0","type":"link"}