Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 13
Sivu 5
... described by such surfaces . * In general , * The mapping which takes all points having one or more irrational coordinates into category 1 and all other points ( i.e. , points all of whose coordinates are rational ) into category 2 is ...
... described by such surfaces . * In general , * The mapping which takes all points having one or more irrational coordinates into category 1 and all other points ( i.e. , points all of whose coordinates are rational ) into category 2 is ...
Sivu 100
... described , in that adjustments to the weight vectors are made only when a pattern in the training set is classified ... describing the rule for modifying the weight vectors we shall make use of the notation where P Nk - sgn ( W ( ) . Yk ) ...
... described , in that adjustments to the weight vectors are made only when a pattern in the training set is classified ... describing the rule for modifying the weight vectors we shall make use of the notation where P Nk - sgn ( W ( ) . Yk ) ...
Sivu 125
... described by the phrase " learning without a teacher . " ) The mode - seeking training rule described in Sec . 7-6 was originally proposed and tested by Stark , Okajima , and Whipple . ' They applied the rule in a simulated PWL machine ...
... described by the phrase " learning without a teacher . " ) The mode - seeking training rule described in Sec . 7-6 was originally proposed and tested by Stark , Okajima , and Whipple . ' They applied the rule in a simulated PWL machine ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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adjusted apply assume bank called cells changes Chapter classifier cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern hyperplane pattern space pattern vector pattern-classifying 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 |