Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 26
Sivu 72
... rule ; we shall do so for a set of three - dimensional pat- terns with binary components using the fixed - increment correction rule with c = 1. The training set of augmented pattern vectors and their de- sired responses is shown below ...
... rule ; we shall do so for a set of three - dimensional pat- terns with binary components using the fixed - increment correction rule with c = 1. The training set of augmented pattern vectors and their de- sired responses is shown below ...
Sivu 101
... rule is used to adjust 1⁄2 ( | N | + 1 ) of the ( P + Nk ) / 2 weight vectors making nonnegative dot products with Y. Those 2 ( N + 1 ) having the least - positive ( but not negative ) dot prod- ucts are adjusted by the rule W1 ( k + 1 ) ...
... rule is used to adjust 1⁄2 ( | N | + 1 ) of the ( P + Nk ) / 2 weight vectors making nonnegative dot products with Y. Those 2 ( N + 1 ) having the least - positive ( but not negative ) dot prod- ucts are adjusted by the rule W1 ( k + 1 ) ...
Sivu 133
... rule , 82 , 85 of fractional correction rule , 91 of generalized error - correction rule , 89 , 90 Convergence theorem , perceptron , 79 Convexity of decision regions , 20 Cooper , 62 , 63 Correction increment , 69 , 75 , 80 , 101 ...
... rule , 82 , 85 of fractional correction rule , 91 of generalized error - correction rule , 89 , 90 Convergence theorem , perceptron , 79 Convexity of decision regions , 20 Cooper , 62 , 63 Correction increment , 69 , 75 , 80 , 101 ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
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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 |