Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 70
... rule , the absolute correction rule , the TLU response will agree with the desired response . It is readily seen that the absolute correction rule leads to the same results as does the fixed - increment rule with c = 1 if , in the ...
... rule , the absolute correction rule , the TLU response will agree with the desired response . It is readily seen that the absolute correction rule leads to the same results as does the fixed - increment rule with c = 1 if , in the ...
Sivu 77
... rule at substantially the same time . Ridgway 10 later suggested a modification of the Widrow - Hoff rule which rendered it sub- stantially the same as the absolute correction rule . Motzkin and Schoen- berg11 proposed what we have ...
... rule at substantially the same time . Ridgway 10 later suggested a modification of the Widrow - Hoff rule which rendered it sub- stantially the same as the absolute correction rule . Motzkin and Schoen- berg11 proposed what we have ...
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 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
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adjusted apply assume bank called cells changes Chapter cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described dichotomies discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented important initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern classifier pattern hyperplane pattern space pattern vector 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 |