Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 22
Sivu 52
... zero , the contours of equal probability are circles ( zero eccentricity ) . The expression for the bivariate normal density function for the unnormalized and untranslated variables 1 and 2 is more complicated * than that of Eq . ( 3 ...
... zero , the contours of equal probability are circles ( zero eccentricity ) . The expression for the bivariate normal density function for the unnormalized and untranslated variables 1 and 2 is more complicated * than that of Eq . ( 3 ...
Sivu 70
... zero . When c is equal to one , for example , each weight is altered by the addition ( or subtraction ) of the ... zero . In one case , c is taken to be the smallest integer which will make the value of W Y cross the threshold of zero ...
... zero . When c is equal to one , for example , each weight is altered by the addition ( or subtraction ) of the ... zero . In one case , c is taken to be the smallest integer which will make the value of W Y cross the threshold of zero ...
Sivu 109
... zero vector is a vertex belonging to 1 ( ) ; otherwise is chosen to be any convenient positive number . In this way all the pattern vectors which map into the image - space zero vector are automatically classified correctly . Clearly ...
... zero vector is a vertex belonging to 1 ( ) ; otherwise is chosen to be any convenient positive number . In this way all the pattern vectors which map into the image - space zero vector are automatically classified correctly . Clearly ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
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adjusted apply assume bank called cells changes Chapter cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described dichotomies discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented important initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern classifier pattern hyperplane pattern space pattern vector 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 |