Kernel Methods for Pattern AnalysisCambridge University Press, 28 cze 2004 - 462 Kernel methods provide a powerful and unified framework for pattern discovery, motivating algorithms that can act on general types of data (e.g. strings, vectors or text) and look for general types of relations (e.g. rankings, classifications, regressions, clusters). The application areas range from neural networks and pattern recognition to machine learning and data mining. This book, developed from lectures and tutorials, fulfils two major roles: firstly it provides practitioners with a large toolkit of algorithms, kernels and solutions ready to use for standard pattern discovery problems in fields such as bioinformatics, text analysis, image analysis. Secondly it provides an easy introduction for students and researchers to the growing field of kernel-based pattern analysis, demonstrating with examples how to handcraft an algorithm or a kernel for a new specific application, and covering all the necessary conceptual and mathematical tools to do so. |
Spis treści
XII | 27 |
XIII | 36 |
XIV | 42 |
XV | 43 |
XVI | 44 |
XVII | 45 |
XVIII | 47 |
XIX | 48 |
XX | 60 |
XXI | 68 |
XXII | 74 |
XXIII | 82 |
XXV | 85 |
XXVI | 86 |
XXVII | 93 |
XXVIII | 97 |
XXIX | 104 |
XXX | 105 |
XXXI | 106 |
XXXII | 109 |
XXXIII | 111 |
XXXIV | 112 |
XXXV | 122 |
XXXVI | 128 |
XXXVII | 132 |
XXXVIII | 137 |
XXXIX | 138 |
XL | 140 |
XLI | 141 |
XLII | 143 |
XLIII | 155 |
XLIV | 161 |
XLV | 164 |
XLVI | 176 |
XLVIII | 192 |
XLIX | 193 |
L | 195 |
LI | 196 |
LII | 211 |
LXI | 286 |
LXIII | 289 |
LXIV | 291 |
LXV | 292 |
LXVI | 297 |
LXVII | 304 |
LXVIII | 310 |
LXIX | 314 |
LXX | 318 |
LXXI | 320 |
LXXII | 322 |
LXXIII | 324 |
LXXIV | 325 |
LXXV | 327 |
LXXVI | 328 |
LXXVII | 331 |
LXXVIII | 341 |
LXXIX | 342 |
LXXX | 344 |
LXXXI | 345 |
LXXXII | 347 |
LXXXIII | 351 |
LXXXIV | 357 |
LXXXV | 360 |
LXXXVI | 372 |
LXXXVII | 382 |
LXXXVIII | 395 |
XC | 397 |
XCI | 398 |
XCII | 421 |
XCIII | 435 |
XCIV | 436 |
XCV | 437 |
XCVI | 444 |
XCVII | 446 |
XCVIII | 448 |
XCIX | 450 |
460 | |
Inne wydania - Wyświetl wszystko
Kluczowe wyrazy i wyrażenia
ANOVA kernel apply approach bound centre of mass Chapter Cholesky decomposition classification clustering Code Fragment columns computation consider corresponding covariance data items dataset decomposition defined Definition denote derived dimension distribution document dual eigenvalues eigenvectors embedding entries equation evaluation example feature mapping feature space feature vectors Fisher kernel generalisation given gives graph Hence hidden Markov model hypersphere implemented inner product kernel function kernel matrix kernel methods kernel PCA kernel-defined feature space labelled learning linear function loss function maximise minimise node norm normalised novelty-detection obtain optimisation problem orthogonal output pair parameters pattern analysis algorithm pattern function polynomial kernel positive semi-definite primal probability projection properties Pseudocode random ranking recursion regularisation relations Remark representation result ridge regression semantic sequence slack variables soft margin solution solving statistical strings structure subset subspace substrings support vector machine support vector regression symbols Theorem training set tree trie-based updates variance weight vector