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 + · • + WмfM + 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 + · • + WмfM + 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 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
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adjusted apply assume bank belonging to category called changes Chapter cluster committee components consider consists contains correction corresponding decision surfaces define denote density depends derivation described Development discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed given implemented important initial layered machine linear dichotomies linear discriminant functions linear machine linearly separable measurements negative networks normal Note optimum origin parameters partition pattern classifier pattern hyperplane pattern space pattern vector piecewise linear plane points positive presented probability problem proof properties proved PWL machine quadric reduced regions respect response rule sample mean selection separable shown side space Stanford step subsidiary discriminant Suppose theorem theory threshold training methods training procedure training sequence training subsets transformation values weight vectors zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |