Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 49
Sivu 72
... example of error - correction training is illustrated in Fig . 4-2 . There are four patterns represented by pattern hyperplanes in weight space . The small arrows attached to these planes in this case indicate the side on which a TLU ...
... example of error - correction training is illustrated in Fig . 4-2 . There are four patterns represented by pattern hyperplanes in weight space . The small arrows attached to these planes in this case indicate the side on which a TLU ...
Sivu 101
... example in which we have three augmented patterns of two dimensions . 6.4 An example The training procedure described above can be illustrated quite clearly by a two - dimensional example . The geometrical interpretation of this ...
... example in which we have three augmented patterns of two dimensions . 6.4 An example The training procedure described above can be illustrated quite clearly by a two - dimensional example . The geometrical interpretation of this ...
Sivu 105
... example , pat- terns 3 and 7 both yield a response of +1 for TLU 1 and a response of -1 for the other TLUS ; hence these two patterns are transformed into the single point ( 1 , -1 , -1 ) in image space . The numbers associated with the ...
... example , pat- terns 3 and 7 both yield a response of +1 for TLU 1 and a response of -1 for the other TLUS ; hence these two patterns are transformed into the single point ( 1 , -1 , -1 ) in image space . The numbers associated with the ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
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adjusted apply assume bank called cells changes Chapter classifier cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern hyperplane pattern space pattern vector pattern-classifying 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 |