Kernel Methods for Pattern AnalysisCambridge University Press, 2004. gada 28. jūn. - 462 lappuses Pattern Analysis is the process of finding general relations in a set of data, and forms the core of many disciplines, from neural networks, to so-called syntactical pattern recognition, from statistical pattern recognition to machine learning and data mining. Applications of pattern analysis range from bioinformatics to document retrieval. The kernel methodology described here provides a powerful and unified framework for all of these disciplines, motivating algorithms that can act on general types of data (e.g. strings, vectors, text, etc.) and look for general types of relations (e.g. rankings, classifications, regressions, clusters, etc.). This book fulfils two major roles. Firstly it provides practitioners with a large toolkit of algorithms, kernels and solutions ready to be implemented many given as Matlab code, suitable for many pattern analysis tasks in fields such as bioinformatics, text analysis, and image analysis. Secondly it furnishes students and researchers with an easy introduction to the rapidly expanding field of kernel-based pattern analysis, demonstrating with examples how to handcraft an algorithm or a kernel for a new specific application, while covering the required conceptual and mathematical tools necessary to do so. The book is in three parts. The first provides the conceptual foundations of the field, both by giving an extended introductory example, and by covering the main theoretical underpinnings of the approach. The second part contains a number of kernel-based algorithms, from the simplest to sophisticated systems such as kernel partial least squares, canonical correlation analysis, support vector machines, principal components analysis, etc. The final part describes a number of kernel functions, from basic examples to advanced recursive kernels, kernels derived from generative models such as HMMs and string matching kernels based on dynamic programming, as well as special kernels designed to handle text documents. All those involved in pattern recognition, machine learning, neural networks and their applications, from computational biology to text analysis will welcome this account. |
No grāmatas satura
1.–5. rezultāts no 72.
xii. lappuse
Atvainojiet, šīs lappuses saturs ir ierobežots..
Atvainojiet, šīs lappuses saturs ir ierobežots..
30. lappuse
Atvainojiet, šīs lappuses saturs ir ierobežots..
Atvainojiet, šīs lappuses saturs ir ierobežots..
34. lappuse
Atvainojiet, šīs lappuses saturs ir ierobežots..
Atvainojiet, šīs lappuses saturs ir ierobežots..
36. lappuse
Atvainojiet, šīs lappuses saturs ir ierobežots..
Atvainojiet, šīs lappuses saturs ir ierobežots..
45. lappuse
Atvainojiet, šīs lappuses saturs ir ierobežots..
Atvainojiet, šīs lappuses saturs ir ierobežots..
Saturs
IV | 3 |
V | 4 |
VI | 10 |
VII | 15 |
VIII | 20 |
IX | 21 |
X | 23 |
XI | 24 |
LIV | 217 |
LV | 227 |
LVI | 235 |
LVII | 236 |
LVIII | 238 |
LX | 239 |
LXI | 249 |
LXII | 262 |
XII | 25 |
XIII | 34 |
XIV | 40 |
XV | 41 |
XVI | 42 |
XVII | 43 |
XVIII | 43 |
XIX | 44 |
XX | 55 |
XXI | 62 |
XXII | 68 |
XXIII | 76 |
XXV | 79 |
XXVI | 80 |
XXVII | 87 |
XXVIII | 91 |
XXIX | 98 |
XXX | 99 |
XXXI | 100 |
XXXII | 103 |
XXXIV | 105 |
XXXV | 106 |
XXXVI | 116 |
XXXVII | 120 |
XXXVIII | 124 |
XXXIX | 129 |
XL | 130 |
XLI | 132 |
XLIII | 133 |
XLIV | 135 |
XLV | 147 |
XLVI | 153 |
XLVII | 156 |
XLVIII | 166 |
XLIX | 180 |
L | 181 |
LI | 183 |
LII | 184 |
LIII | 199 |
LXIII | 268 |
LXV | 271 |
LXVI | 273 |
LXVIII | 274 |
LXIX | 279 |
LXX | 286 |
LXXI | 292 |
LXXII | 296 |
LXXIII | 298 |
LXXIV | 300 |
LXXV | 302 |
LXXVI | 304 |
LXXVII | 305 |
LXXVIII | 307 |
LXXX | 308 |
LXXXI | 311 |
LXXXII | 321 |
LXXXIII | 322 |
LXXXIV | 324 |
LXXXVI | 325 |
LXXXVII | 327 |
LXXXVIII | 331 |
LXXXIX | 337 |
XC | 340 |
XCI | 352 |
XCII | 360 |
XCIII | 373 |
XCIV | 375 |
XCVI | 376 |
XCVII | 399 |
XCVIII | 413 |
XCIX | 414 |
C | 415 |
CII | 422 |
CIII | 424 |
CV | 426 |
CVII | 428 |
CVIII | 438 |
Citi izdevumi - Skatīt visu
Kernel Methods for Pattern Analysis John Shawe-Taylor,Nello Cristianini Ierobežota priekšskatīšana - 2004 |
Bieži izmantoti vārdi un frāzes
ANOVA kernel apply approach bound centre of mass Chapter Cholesky decomposition classification clustering co-rooted Code Fragment computation consider corresponding covariance data items dataset decomposition defined Definition denote dimension distribution dual eigenvalues eigenvectors embedding entries equation evaluation example feature space feature vectors finite Fisher kernel gap-weighted generalisation given graph Hence hidden Markov model hypersphere inner product kernel function kernel matrix kernel methods kernel PCA kernel-defined feature space labelled learning linear function loss function maximise minimising node norm normalised novelty-detection obtain optimisation problem orthogonal output pair parameters pattern analysis algorithm pattern function positive semi-definite primal probability projection properties Pseudocode random rank recursion regularisation relations Remark representation result ridge regression semantic sequence slack variables soft margin solution statistical strings subsequences kernel subset subspace substrings subtree support vector machine support vector regression Theorem training set tree weight vector
Atsauces uz šo grāmatu
Signal Processing of Power Quality Disturbances Math H. J. Bollen,Irene Y. H. Gu Ierobežota priekšskatīšana - 2006 |
Reviews in Computational Chemistry, Volume 23 Kenny B. Lipkowitz,Thomas R. Cundari,Donald B. Boyd Priekšskatījums nav pieejams - 2007 |