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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assume augmented pattern belonging to category Chapter cluster committee machine committee TLUS components correction increment covariance matrix d-dimensional decision surfaces denote diagonal matrix discussed dot products error-correction procedure Euclidean distance example Fix and Hodges function g(X g₁(X given Hodges method hypersphere image-space implemented initial weight vectors ith bank layer of TLUS layered machine linear dichotomies linear discriminant functions linearly separable loss function mean vector minimum-distance classifier mode-seeking networks nonparametric number of patterns p₁ parameters parametric training partition pattern hyperplane pattern points pattern space pattern vector pattern-classifying patterns belonging perceptron piecewise linear plane point sets positive probability distributions prototype pattern PWL machine quadratic form quadric function rule sample covariance matrix shown in Fig solution weight vectors subsets X1 subsidiary discriminant functions Suppose terns training patterns training sequence training set training subsets transformation two-layer machine values W₁ weight point weight space weight-vector sequence X1 and X2 zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |