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 boostingthe 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, nonnegative 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 codeveloped much of the statistical modeling software and environment in R/SPLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is coauthor of the very successful An Introduction to the Bootstrap. Friedman is the coinventor of many datamining tools including CART, MARS, projection pursuit and gradient boosting. 
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Manual
I own the 2nd edition of this book. The topics are described more from a statistics perspective than the computer science perspective, but as written by statisticians for computer scientists instead of for other statisticians. The examples are interesting and the graphics very nice.
Contents
II  1 
III  9 
VII  11 
VIII  18 
IX  22 
X  28 
XI  32 
XII  33 
LXXX  254 
LXXXI  255 
LXXXII  257 
LXXXV  266 
LXXXVI  279 
LXXXVII  283 
LXXXVIII  290 
LXXXIX  293 
XIII  37 
XIV  39 
XVI  41 
XVII  42 
XVIII  50 
XIX  55 
XX  75 
XXIII  79 
XXIV  81 
XXV  84 
XXVI  95 
XXVII  105 
XXVIII  111 
XXX  115 
XXXIII  117 
XXXIV  126 
XXXV  127 
XXXVI  134 
XXXVII  137 
XXXVIII  138 
XXXIX  144 
XL  148 
XLI  155 
XLIII  160 
XLV  163 
XLVI  165 
XLIX  172 
L  174 
LI  175 
LII  179 
LIII  182 
LIV  186 
LV  188 
LVI  190 
LIX  193 
LX  196 
LXI  200 
LXII  203 
LXIII  205 
LXIV  206 
LXV  208 
LXVI  210 
LXVII  214 
LXVIII  217 
LXIX  222 
LXXI  225 
LXXIII  231 
LXXIV  235 
LXXV  236 
LXXVI  243 
LXXVII  246 
LXXVIII  250 
LXXIX  253 
XC  295 
XCII  296 
XCIII  299 
XCIV  303 
XCV  304 
XCVI  305 
XCVII  306 
XCVIII  308 
XCIX  312 
C  314 
CI  316 
CII  319 
CIII  323 
CIV  324 
CV  331 
CVI  335 
CVII  340 
CVIII  344 
CIX  347 
CXI  350 
CXII  353 
CXIII  355 
CXIV  359 
CXV  362 
CXVI  366 
CXVII  367 
CXIX  368 
CXX  371 
CXXII  377 
CXXIII  390 
CXXIV  391 
CXXV  397 
CXXVI  399 
CXXVII  406 
CXXIX  411 
CXXXI  415 
CXXXII  427 
CXXXIII  432 
CXXXIV  433 
CXXXVI  437 
CXXXIX  439 
CXL  453 
CXLI  480 
CXLII  485 
CXLIII  494 
CXLIV  502 
CXLV  503 
CXLVI  504 
509  
CXLVIII  523 
527  