Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 10
Sivu 5
... described by such surfaces . * In general , * The mapping which takes all points having one or more irrational coordinates into category 1 and all other points ( i.e. , points all of whose coordinates are rational ) into category 2 is ...
... described by such surfaces . * In general , * The mapping which takes all points having one or more irrational coordinates into category 1 and all other points ( i.e. , points all of whose coordinates are rational ) into category 2 is ...
Sivu 52
... described . The contours of equal probability density are still ellipses , and the picture is much like Fig . 3.2 except for a translation and stretch- ing of the z1 and z2 axes . In general , the elliptical cross sections are centered ...
... described . The contours of equal probability density are still ellipses , and the picture is much like Fig . 3.2 except for a translation and stretch- ing of the z1 and z2 axes . In general , the elliptical cross sections are centered ...
Sivu 125
... described by the phrase " learning without a teacher . " ) 7 = The mode - seeking training rule described in Sec . 7-6 was originally proposed and tested by Stark , Okajima , and Whipple . They applied the rule in a simulated PWL ...
... described by the phrase " learning without a teacher . " ) 7 = The mode - seeking training rule described in Sec . 7-6 was originally proposed and tested by Stark , Okajima , and Whipple . They applied the rule in a simulated PWL ...
Sisältö
Preface vii | 11 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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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 important 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 terns theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors Y₁ zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |