Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 1
... example of a sorting task is weather prediction . A forecast must be based on certain weather measurements , for example , the present values of atmospheric pressure and atmospheric pressure changes at a number of stations . Suppose ...
... example of a sorting task is weather prediction . A forecast must be based on certain weather measurements , for example , the present values of atmospheric pressure and atmospheric pressure changes at a number of stations . Suppose ...
Sivu 72
... example of error - correction training is illustrated in Fig . 4-2 . There are four patterns represented by pattern hyperplanes in weight space . The small arrows attached to these planes in this case indicate the side on which a TLU ...
... example of error - correction training is illustrated in Fig . 4-2 . There are four patterns represented by pattern hyperplanes in weight space . The small arrows attached to these planes in this case indicate the side on which a TLU ...
Sivu 101
... example in which we have three augmented patterns of two dimensions . 6.4 An example The training procedure described above can be illustrated quite clearly by a two - dimensional example . The geometrical interpretation of this ...
... example in which we have three augmented patterns of two dimensions . 6.4 An example The training procedure described above can be illustrated quite clearly by a two - dimensional example . The geometrical interpretation of this ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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assume belonging to category cluster committee machine committee TLUS components correction increment covariance matrix decision surfaces denote diagonal matrix dot products error-correction procedure Euclidean distance example Fix and Hodges function g(X g₁(X gi(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 parametric training partition pattern hyperplane pattern points pattern space pattern vector pattern-classifying patterns belonging perceptron piecewise linear point sets positive probability distributions prototype pattern PWL machine quadratic form quadric function rule sample covariance matrix shown in Fig solution weight vectors subsets X1 subsidiary discriminant functions Suppose terns TLU response training patterns training sequence training set training subsets transformation two-layer machine values W₁ wa+1 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 |