Index
3-term DNF, 107
F 1 -score, 244
` 1 norm, 183, 332, 363, 386
accuracy, 38, 43
activation function, 269
AdaBoost, 130, 134 , 362
all-pairs, 228, 404
approximation error, 61, 64
auto-encoders, 368
backpropagation, 278
backward elimination, 363
bag-of-words, 209
base hypothesis, 137
Bayes optimal, 46, 52, 260
Bayes rule, 354
Bayesian reasoning, 353
Bennet’s inequality, 426
Bernstein’s inequality, 426
bias, 37, 61, 64
bias-complexity tradeoff, 65
boolean conjunctions, 51, 79, 106
boosting, 130
boosting the confidence, 142
boundedness, 165
C4.5, 254
CART, 254
chaining, 389
Chebyshev’s inequality, 423
Chernoff bounds, 423
class-sensitive feature mapping, 230
classifier, 34
clustering, 307
spectral, 315
compressed sensing, 330
compression bounds, 410
compression scheme, 411
computational complexity, 100
confidence, 38, 43
consistency, 92
Consistent, 289
contraction lemma, 381
convex, 156
function, 157
set, 156
strongly convex, 174, 195
convex-Lipschitz-bounded learning, 166
convex-smooth-bounded learning, 166
covering numbers, 388
curse of dimensionality, 263
decision stumps, 132, 133
decision trees, 250
dendrogram, 309, 310
dictionary learning, 368
differential set, 188
dimensionality reduction, 323
discretization trick, 57
discriminative, 342
distribution free, 342
domain, 33
domain of examples, 48
doubly stochastic matrix, 242
duality, 211
strong duality, 211
weak duality, 211
Dudley classes, 81
efficient computable, 100
EM, 348
empirical error, 35
empirical risk, 35, 48
Empirical Risk Minimization,seeERM
entropy, 345
relative entropy, 345
epigraph, 157
ERM, 35
error decomposition, 64, 168
estimation error, 61, 64
Expectation-Maximization,seeEM
face recognition,seeViola-Jones
feasible, 100
feature, 33
feature learning, 368
feature normalization, 365
feature selection, 357, 358
feature space, 215
feature transformations, 367
filters, 359Understanding Machine Learning,©c2014 by Shai Shalev-Shwartz and Shai Ben-David
Published 2014 by Cambridge University Press.
Personal use only. Not for distribution. Do not post.
Please link tohttp://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning