Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 25
Sivu 28
... quadratic form will never be negative , and it and A are called positive semidefinite . 2.9 Quadric decision surfaces The decision surfaces of quadric machines are sections of second - degree surfaces which we shall call quadric ...
... quadratic form will never be negative , and it and A are called positive semidefinite . 2.9 Quadric decision surfaces The decision surfaces of quadric machines are sections of second - degree surfaces which we shall call quadric ...
Sivu 29
... quadric discriminant function of X there corresponds a linear discriminant function of F. Equation ( 2 · 21 ) can therefore be written as g ( X ) = wifi + w2f2 ++ WMƒM + WM + 1 ( 2.28 ) The implementation of a quadric discriminator ...
... quadric discriminant function of X there corresponds a linear discriminant function of F. Equation ( 2 · 21 ) can therefore be written as g ( X ) = wifi + w2f2 ++ WMƒM + WM + 1 ( 2.28 ) The implementation of a quadric discriminator ...
Sivu 30
... quadric machine can therefore be implemented by a quadric processor followed by a linear machine . 2.11 Φ functions We noted in Sec . 2.10 that a quadric discriminant function can be con- sidered to be a linear function of the ...
... quadric machine can therefore be implemented by a quadric processor followed by a linear machine . 2.11 Φ functions We noted in Sec . 2.10 that a quadric discriminant function can be con- sidered to be a linear function of the ...
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 |