Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 55
... quadratic form . The surfaces defined by setting this quadratic form equal ... function p ( Xi ) that apply . That is , we know R normal density functions ... function , the opti- mum classifier uses the discriminant functions given by gi ...
... quadratic form . The surfaces defined by setting this quadratic form equal ... function p ( Xi ) that apply . That is , we know R normal density functions ... function , the opti- mum classifier uses the discriminant functions given by gi ...
Sivu 127
... FUNCTIONS A 1 Separation of a quadratic form into positive and negative parts Consider the quadric function g ( X ) = X'AX + B'X + C ( A - 1 ) where A is a real , d X d , symmetric matrix , B is a d - dimensional column vector , and C ...
... FUNCTIONS A 1 Separation of a quadratic form into positive and negative parts Consider the quadric function g ( X ) = X'AX + B'X + C ( A - 1 ) where A is a real , d X d , symmetric matrix , B is a d - dimensional column vector , and C ...
Sivu 136
... functions , 44 Probability functions , 43 Probabilistic pattern sets , 43 Prototype pattern , 18 , 52 Quadratic form , 27 , 56 , 127 eigenvalues of , 27 , 128 Quadric decision surfaces , 28 , 38 equation of , 28 names of , 28 Quadric ...
... functions , 44 Probability functions , 43 Probabilistic pattern sets , 43 Prototype pattern , 18 , 52 Quadratic form , 27 , 56 , 127 eigenvalues of , 27 , 128 Quadric decision surfaces , 28 , 38 equation of , 28 names of , 28 Quadric ...
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 step subsidiary discriminant Suppose terns 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 |