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 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
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adjusted apply assume bank belonging to category called changes Chapter cluster committee components consider consists contains correction corresponding decision surfaces define denote density depends derivation described Development discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed given implemented important initial layered machine linear dichotomies linear discriminant functions linear machine linearly separable measurements negative networks normal Note optimum origin parameters partition pattern classifier pattern hyperplane pattern space pattern vector piecewise linear plane points positive presented probability problem proof properties proved PWL machine quadric reduced regions respect response rule sample mean selection separable shown side space Stanford step subsidiary discriminant Suppose theorem theory threshold training methods training procedure training sequence training subsets transformation values weight vectors zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |