Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 9
... known as training . The training process proceeds as follows : a large number of patterns are chosen as typical of those which the machine must ultimately classify . This set of patterns is called the training set . The desired ...
... known as training . The training process proceeds as follows : a large number of patterns are chosen as typical of those which the machine must ultimately classify . This set of patterns is called the training set . The desired ...
Sivu 44
... known functions of a finite number of characteristic parameters whose values we might not know a priori . For example , we may know that the p ( X | i ) , i = 1 , . . . , R , are normal probability - density functions with unknown means ...
... known functions of a finite number of characteristic parameters whose values we might not know a priori . For example , we may know that the p ( X | i ) , i = 1 , . . . , R , are normal probability - density functions with unknown means ...
Sivu 59
... known but for which the mean vectors are assumed to be random variables . Suppose the pattern vectors belonging to category i are normal with known covariance matrix Σ ; and unknown mean vector . Thus , the d com- ponents of the mean ...
... known but for which the mean vectors are assumed to be random variables . Suppose the pattern vectors belonging to category i are normal with known covariance matrix Σ ; and unknown mean vector . Thus , the d com- ponents of the mean ...
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 |