Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 6
Foundations of Trainable Pattern-classifying Systems Nils J. Nilsson. the decision surfaces divide Ed into R regions which we shall call decision regions . The ith region R ; is the set of points which map into the ith cate- gory ...
Foundations of Trainable Pattern-classifying Systems Nils J. Nilsson. the decision surfaces divide Ed into R regions which we shall call decision regions . The ith region R ; is the set of points which map into the ith cate- gory ...
Sivu 7
... decision surfaces do not uniquely specify the discriminant functions . For one thing , the same arbitrary constant can be added to each discriminant function without altering the implied decision surfaces . In general , any monotonic ...
... decision surfaces do not uniquely specify the discriminant functions . For one thing , the same arbitrary constant can be added to each discriminant function without altering the implied decision surfaces . In general , any monotonic ...
Sivu 18
... decision surfaces of linear machines Suppose that two decision regions R ; and R , of a linear machine share a common boundary . The decision surface separating these two regions is then a segment of the surface Si ; having the equation ...
... decision surfaces of linear machines Suppose that two decision regions R ; and R , of a linear machine share a common boundary . The decision surface separating these two regions is then a segment of the surface Si ; having the equation ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
Tekijänoikeudet | |
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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 |