Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 37
Sivu 28
... called positive definite . If A has one or more of its eigenvalues equal to zero and all the others positive , then the quadratic form will never be negative , and it and A are called positive semidefinite . 2.9 Quadric decision ...
... called positive definite . If A has one or more of its eigenvalues equal to zero and all the others positive , then the quadratic form will never be negative , and it and A are called positive semidefinite . 2.9 Quadric decision ...
Sivu 67
... called the pattern hyperplane . This hyperplane separates the space of weight points into two classes : Those which for the pattern Y produce a TLU response of one are on one side of the hyperplane , called the positive side , and those ...
... called the pattern hyperplane . This hyperplane separates the space of weight points into two classes : Those which for the pattern Y produce a TLU response of one are on one side of the hyperplane , called the positive side , and those ...
Sivu 82
... called linearly contained if and only if there exists a vector W , called a solution weight vector , such that Y.W > 0 for each Y in y ' ( 5.6 ) Clearly , y ' is linearly contained if and only if y1 and Y2 are linearly separable ...
... called linearly contained if and only if there exists a vector W , called a solution weight vector , such that Y.W > 0 for each Y in y ' ( 5.6 ) Clearly , y ' is linearly contained if and only if y1 and Y2 are linearly separable ...
Sisältö
Preface vii | 11 |
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 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 |