Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 12
Sivu 99
... 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 ) FIGURE 6.4 A committee machine 6.3 A training procedure for committee ...
... 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 ) FIGURE 6.4 A committee machine 6.3 A training procedure for committee ...
Sivu 100
... committee TLUS would have posi- tive responses , and the machine would respond correctly to Yk . For ex- ample , if exactly seven TLUS in a committee of size nine had negative responses to Yk , then N -5 . At least three of the seven ...
... committee TLUS would have posi- tive responses , and the machine would respond correctly to Yk . For ex- ample , if exactly seven TLUS in a committee of size nine had negative responses to Yk , then N -5 . At least three of the seven ...
Sivu 103
... 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 with initial weight vectors of approximately the same length . Sup- pose that W2 ...
... 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 with initial weight vectors of approximately the same length . Sup- pose that W2 ...
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 |