Pattern RecognitionAcademic Press, 26 nov 2008 - 984 páginas This book considers classical and current theory and practice, of supervised, unsupervised and semi-supervised pattern recognition, to build a complete background for professionals and students of engineering. The authors, leading experts in the field of pattern recognition, have provided an up-to-date, self-contained volume encapsulating this wide spectrum of information. The very latest methods are incorporated in this edition: semi-supervised learning, combining clustering algorithms, and relevance feedback. · Thoroughly developed to include many more worked examples to give greater understanding of the various methods and techniques · Many more diagrams included--now in two color--to provide greater insight through visual presentation · Matlab code of the most common methods are given at the end of each chapter. · More Matlab code is available, together with an accompanying manual, via this site · Latest hot topics included to further the reference value of the text including non-linear dimensionality reduction techniques, relevance feedback, semi-supervised learning, spectral clustering, combining clustering algorithms. · An accompanying book with Matlab code of the most common methods and algorithms in the book, together with a descriptive summary, and solved examples including real-life data sets in imaging, and audio recognition. The companion book will be available separately or at a special packaged price (ISBN: 9780123744869).
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Índice
| 1 | |
| 13 | |
| 91 | |
Chapter 4 Nonlinear Classifiers | 151 |
Chapter 5 Feature Selection | 261 |
Data Transformation and Dimensionality Reduction | 323 |
Chapter 7 Feature Generation II | 411 |
Chapter 8 Template Matching | 481 |
Sequential Algorithms | 627 |
Hierarchical Algorithms | 653 |
Schemes Based on Function Optimization | 701 |
Chapter 15 Clustering Algorithms IV | 765 |
Chapter 16 Cluster Validity | 863 |
Appendix A Hints from Probabilityand Statistics | 915 |
Appendix B Linear Algebra Basics | 927 |
Appendix C Cost Function Optimization | 930 |
Chapter 9 ContextDependent Classification | 521 |
The Epilogue | 567 |
Basic Concepts | 595 |
Appendix D Basic Definitions from Linear Systems Theory | 946 |
| 949 | |
Términos y frases comunes
Analysis and Machine approximation assume Bayesian Chapter classifier clustering algorithm compute considered constraints convergence corresponding cost function covariance matrix criterion data set defined dendrogram denoted density dimensional dissimilarity eigenvalues eigenvectors equal estimate Euclidean distance example feature space feature vectors Figure filters fuzzy Gaussian given graph hyperplane IEEE IEEE Transactions input iteration kernel l-dimensional learning linear linear classifier link algorithm Machine Intelligence,Vol MATLAB maximum mean value measure method minimize minimum multilayer perceptron neural networks Neural Networks,Vol node nonlinear normal distributions number of clusters obtained optimal output path Pattern Analysis pattern recognition perceptron performance pixels points problem procedure Processing Processing,Vol random variable Recognition,Vol regions representatives respective samples scheme Section sequence solution statistical subspace support vector support vector machines task techniques Transactions on Neural Transactions on Pattern transform two-dimensional variance VC dimension weights zero