Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu ix
... discriminant functions , 15 2.2 Linear discriminant functions , 16 2.3 Minimum - distance classifiers , 16 2.4 The decision surfaces of linear machines , 18 2.5 Linear classifications of patterns , 20 2.6 The threshold logic unit ( TLU ) ...
... discriminant functions , 15 2.2 Linear discriminant functions , 16 2.3 Minimum - distance classifiers , 16 2.4 The decision surfaces of linear machines , 18 2.5 Linear classifications of patterns , 20 2.6 The threshold logic unit ( TLU ) ...
Sivu 16
... discriminant functions belonging to a particular family can then be accomplished by adjusting the values of the parame- ters . We shall often call these ... DISCRIMINANT FUNCTIONS Linear discriminant functions, Minimum-distance classifiers,
... discriminant functions belonging to a particular family can then be accomplished by adjusting the values of the parame- ters . We shall often call these ... DISCRIMINANT FUNCTIONS Linear discriminant functions, Minimum-distance classifiers,
Sivu 24
... discriminant function , is given by an expression of the form g . ( X ) = Wilx1 + Wi2DX2 + + WidXa + wid + 1 ( 2 · 19 ) X H w il w il +1 w Pattern il 24 SOME IMPORTANT DISCRIMINANT FUNCTIONS Piecewise linear discriminant functions,
... discriminant function , is given by an expression of the form g . ( X ) = Wilx1 + Wi2DX2 + + WidXa + wid + 1 ( 2 · 19 ) X H w il w il +1 w Pattern il 24 SOME IMPORTANT DISCRIMINANT FUNCTIONS Piecewise linear discriminant functions,
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 |