Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 25
Sivu 27
... Quadric discriminant functions A quadric discriminant function has the form d d - 1 d 9 : ( X ) = Σ w , x , 2 + Σ Σ week + WjkXjXk + Σ W¡X¡ + Wa + 1 ( 2.21 ) j = 1 j = 1 k = j + 1 J = 1 Any machine which employs quadric discriminant ...
... Quadric discriminant functions A quadric discriminant function has the form d d - 1 d 9 : ( X ) = Σ w , x , 2 + Σ Σ week + WjkXjXk + Σ W¡X¡ + Wa + 1 ( 2.21 ) j = 1 j = 1 k = j + 1 J = 1 Any machine which employs quadric discriminant ...
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 + + wMJM + 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 + + wMJM + WM + 1 ( 2.28 ) The implementation of a quadric discriminator ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
Tekijänoikeudet | |
3 muita osia ei näytetty
Muita painoksia - Näytä kaikki
Yleiset termit ja lausekkeet
assume augmented pattern belonging to category Chapter cluster committee machine committee TLUS correction increment covariance matrix d-dimensional decision surfaces denote diagonal matrix discussed dot products error-correction procedure Euclidean distance example Fix and Hodges function g(X g₁(X given Hodges method hypersphere image-space implemented initial weight vectors ith bank layer of TLUS layered machine linear dichotomies linear discriminant functions linearly separable loss function mean vector minimum-distance classifier mode-seeking networks nonparametric number of patterns p₁ parameters partition pattern classifier pattern hyperplane pattern space pattern vector patterns belonging perceptron piecewise linear plane point sets positive probability distributions prototype pattern PWL machine quadratic form quadric function rule sample covariance matrix shown in Fig solution weight vectors Stanford subsets X1 subsidiary discriminant functions Suppose terns TLU response training patterns training sequence training set training subsets transformation two-layer machine values W₁ weight point weight space weight-vector sequence X1 and X2 zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |