Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Sivu 99
Therefore , our discussion will concentrate on the " simple - majority " committee machine with a fixed vote - taking TLU . X x2 Pattern P committee TLUS * d + 1 = +1 d + 1 Response Vote - taking TLU ( second layer ) ( first layer ) ...
Therefore , our discussion will concentrate on the " simple - majority " committee machine with a fixed vote - taking TLU . X x2 Pattern P committee TLUS * d + 1 = +1 d + 1 Response Vote - taking TLU ( second layer ) ( first layer ) ...
Sivu 100
Since N cannot be even it follows that exactly ( PN ) / 2 of the P committee TLUS have negative responses . If the responses of at least 1⁄2 ( | N✩ | + 1 ) of these negatively responding TLUS were changed from −1 to +1 , then the ...
Since N cannot be even it follows that exactly ( PN ) / 2 of the P committee TLUS have negative responses . If the responses of at least 1⁄2 ( | N✩ | + 1 ) of these negatively responding TLUS were changed from −1 to +1 , then the ...
Sivu 103
Later , another adjustment for Y3 results in a satisfactory location of the three committee weight vectors . At this stage the training process terminates . 1 3 This example can also be used to illustrate the necessity for begin- ning ...
Later , another adjustment for Y3 results in a satisfactory location of the three committee weight vectors . At this stage the training process terminates . 1 3 This example can also be used to illustrate the necessity for begin- ning ...
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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 step subsidiary discriminant Suppose terns 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 |
| Alkuperäisteoksen sijainti | Wisconsinin yliopisto - Madison |
| Digitoitu | 16. huhtikuu 2008 |
| ISBN | 0070465703, 9780070465701 |
| Pituus | 137 sivua |
| Vie sitaatti | BiBTeX EndNote RefMan |