Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 14
Sivu 98
... committee " of weight vectors W1 , W2 , and W , in Fig . 6.3 . With respect to these weight vectors , we have the inequalities W1 . Y1 > 0 • W3 . Y1 > 0 1 W2 . Y1 < 0 W2 1 W1 Y2 > 0 1 W1 . Y > 0 3 · W2 Y2 > 0 W2 . Y3 < 0 ( 6.4 ) • W3 Y2 ...
... committee " of weight vectors W1 , W2 , and W , in Fig . 6.3 . With respect to these weight vectors , we have the inequalities W1 . Y1 > 0 • W3 . Y1 > 0 1 W2 . Y1 < 0 W2 1 W1 Y2 > 0 1 W1 . Y > 0 3 · W2 Y2 > 0 W2 . Y3 < 0 ( 6.4 ) • W3 Y2 ...
Sivu 99
... committee machine with a fixed vote - taking TLU . X Pattern P committee TLUS = +1 d + 1 Response Vote - taking TLU ( second layer ) ( first layer ) FIGURE 6.4 A committee machine 6.3 A training procedure for committee machines Suppose ...
... committee machine with a fixed vote - taking TLU . X Pattern P committee TLUS = +1 d + 1 Response Vote - taking TLU ( second layer ) ( first layer ) FIGURE 6.4 A committee machine 6.3 A training procedure for committee machines Suppose ...
Sivu 100
... committee TLUS would have posi- tive responses , and the machine would respond correctly to Y. 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 TLUS ...
... committee TLUS would have posi- tive responses , and the machine would respond correctly to Y. 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 TLUS ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
11 | 30 |
PARAMETRIC TRAINING METHODS | 43 |
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adjusted apply assume bank called cells changes Chapter classifier cluster column committee machine components Computer 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 terns theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors Y₁ zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |