The Elements of Statistical Learning: Data Mining, Inference, and PredictionDuring the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for wide'' data (p bigger than n), including multiple testing and false discovery rates. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote apopular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting. |
From inside the book
Results 1-5 of 88
... Example : Prostate Cancer . . . 3.2.2 The Gauss - Markov Theorem 3.3.1 Multiple Outputs . . 41 42 47 49 3.4.1 3.4.5 3.4.6 4.1 4.3.2 3.3 Multiple Regression from Simple Univariate Regression . 3.4 Subset Selection and Coefficient ...
... Example : Bias - Variance Tradeoff 7.4 Optimism of the Training Error Rate . 7.5 Estimates of In - Sample Prediction ... Example ( Continued ) 7.10 Cross - Validation 7.11 Bootstrap Methods 7.11.1 Example ( Continued ) Bibliographic ...
... Example ( Continued ) 275 9.3 PRIM - Bump Hunting 279 9.3.1 Spam Example ( Continued ) 282 9.4 MARS : Multivariate Adaptive Regression Splines 283 9.4.1 Spam Example ( Continued ) 287 9.4.2 Example ( Simulated Data ) 288 9.5 ...
... Example : Simulated Data 359 · 11.7 Example : ZIP Code Data 362 11.8 Discussion . . 11.9 Computational Considerations Bibliographic Notes Exercises 12 Support Vector Machines and Flexible Discriminants 12.1 Introduction . . 12.2 The ...
... Example : A Comparative Study 420 13.3.2 Example : k - Nearest - Neighbors and Image Scene Classification 422 13.3.3 Invariant Metrics and Tangent Distance 423 • 13.4 Adaptive Nearest - Neighbor Methods 427 . 13.4.1 Example . . . . 430 ...
Contents
II | 1 |
III | 9 |
IV | 11 |
V | 18 |
VI | 22 |
VII | 28 |
VIII | 32 |
IX | 33 |
LXXII | 254 |
LXXIII | 255 |
LXXIV | 257 |
LXXV | 266 |
LXXVI | 279 |
LXXVII | 283 |
LXXVIII | 290 |
LXXIX | 293 |
X | 37 |
XI | 39 |
XIII | 41 |
XIV | 42 |
XV | 50 |
XVI | 55 |
XVII | 75 |
XX | 79 |
XXI | 81 |
XXII | 84 |
XXIII | 95 |
XXIV | 105 |
XXV | 111 |
XXVII | 115 |
XXVIII | 117 |
XXIX | 126 |
XXX | 127 |
XXXI | 134 |
XXXII | 137 |
XXXIII | 138 |
XXXIV | 144 |
XXXV | 148 |
XXXVI | 155 |
XXXVIII | 160 |
XL | 163 |
XLI | 165 |
XLII | 172 |
XLIII | 174 |
XLIV | 175 |
XLV | 179 |
XLVI | 182 |
XLVII | 186 |
XLVIII | 188 |
XLIX | 190 |
LII | 193 |
LIII | 196 |
LIV | 200 |
LV | 203 |
LVI | 205 |
LVII | 206 |
LVIII | 208 |
LIX | 210 |
LX | 214 |
LXI | 217 |
LXII | 222 |
LXIV | 225 |
LXV | 231 |
LXVI | 235 |
LXVII | 236 |
LXVIII | 243 |
LXIX | 246 |
LXX | 250 |
LXXI | 253 |
LXXX | 295 |
LXXXII | 296 |
LXXXIII | 299 |
LXXXIV | 303 |
LXXXV | 304 |
LXXXVI | 305 |
LXXXVII | 306 |
LXXXVIII | 308 |
LXXXIX | 312 |
XC | 314 |
XCI | 316 |
XCII | 319 |
XCIII | 323 |
XCIV | 324 |
XCV | 331 |
XCVI | 335 |
XCVII | 340 |
XCVIII | 344 |
XCIX | 347 |
C | 350 |
CI | 353 |
CII | 355 |
CIII | 359 |
CIV | 362 |
CV | 366 |
CVI | 367 |
CVIII | 368 |
CIX | 371 |
CX | 377 |
CXI | 390 |
CXII | 391 |
CXIII | 397 |
CXIV | 399 |
CXV | 406 |
CXVII | 411 |
CXVIII | 415 |
CXIX | 427 |
CXX | 432 |
CXXI | 433 |
CXXIII | 437 |
CXXIV | 439 |
CXXV | 453 |
CXXVI | 480 |
CXXVII | 485 |
CXXVIII | 494 |
CXXIX | 502 |
CXXX | 503 |
CXXXI | 504 |
509 | |
CXXXIII | 523 |
527 | |