Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 3
Sivu 22
... weight whose value wa + 1 is energized by a signal of +1 . Usually this +1 signal is associated with the pattern as ... vector w , with components wi , w2 , . . . , wa . The hyperplane equation can then be written as X. w = -Wd + 1 · ( 2-12 ) ...
... weight whose value wa + 1 is energized by a signal of +1 . Usually this +1 signal is associated with the pattern as ... vector w , with components wi , w2 , . . . , wa . The hyperplane equation can then be written as X. w = -Wd + 1 · ( 2-12 ) ...
Sivu 66
... wi , w2 , ... , Wd , Wd + 1 . Training a TLU to dichotomize correctly the ... weight adjust- ments and thus iterative changes in the orientation and ... vector W with components w1 , W2 , ... , Wd , Wa + 1 , extending from the origin to ...
... wi , w2 , ... , Wd , Wd + 1 . Training a TLU to dichotomize correctly the ... weight adjust- ments and thus iterative changes in the orientation and ... vector W with components w1 , W2 , ... , Wd , Wa + 1 , extending from the origin to ...
Sivu 100
... weight vectors is taken to have zero components . The patterns are arranged ... Wi ) , ... , W1 ( k ) , Wp ) have nonnegative dot products with the vector Y ... weight vector lying on a pattern hyperplane is assumed to be on the positive ...
... weight vectors is taken to have zero components . The patterns are arranged ... Wi ) , ... , W1 ( k ) , Wp ) have nonnegative dot products with the vector Y ... weight vector lying on a pattern hyperplane is assumed to be on the positive ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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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 |