Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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To explain the more important implementation we first define the M - dimensional vector F whose components f1 , f2 , of the Xi , i = 1 , " • · · 9 fм are functions d . The first d components of F are x12 , x22 , xa2 ; the next d ( d 1 ) ...
To explain the more important implementation we first define the M - dimensional vector F whose components f1 , f2 , of the Xi , i = 1 , " • · · 9 fм are functions d . The first d components of F are x12 , x22 , xa2 ; the next d ( d 1 ) ...
Sivu 90
inaccurately classified as a member of y , when it actually belongs to Y .; Z is then expressed by Zk = Zijl ( k ) ( 5.33 ) That is , Z is a vector whose ith block of D components is set equal to Y , whose 7th block of D components is ...
inaccurately classified as a member of y , when it actually belongs to Y .; Z is then expressed by Zk = Zijl ( k ) ( 5.33 ) That is , Z is a vector whose ith block of D components is set equal to Y , whose 7th block of D components is ...
Sivu 111
Since U is a binary vector with P components there are 2o distinct U vectors . For some of these U vec- tors , H ( U ) = 1 , and for the remaining , H ( U ) = −1 . Let H1 be a matrix whose rows consist of those U vectors for which H ...
Since U is a binary vector with P components there are 2o distinct U vectors . For some of these U vec- tors , H ( U ) = 1 , and for the remaining , H ( U ) = −1 . Let H1 be a matrix whose rows consist of those U vectors for which H ...
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Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
Tekijänoikeudet | |
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adjusted apply assume bank called cells changes Chapter cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described dichotomies discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented important initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern classifier pattern hyperplane pattern space pattern vector piecewise linear plane points positive presented probability problem properties PWL machine quadric regions respect response rule selection separable sequence side solution space Stanford step subsidiary discriminant Suppose theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors X1 and X2 Y₁ zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |
Kirjaluettelon tiedot
| Otsikko | Learning Machines: Foundations of Trainable Pattern-classifying Systems McGraw-Hill series in systems science |
| Kirjoittaja | Nils J. Nilsson |
| Painos | kuvitettu |
| Kustantaja | McGraw-Hill, 1965 |
| ISBN | 0070465703, 9780070465701 |
| Pituus | 137 sivua |
| Vie sitaatti | BiBTeX EndNote RefMan |