Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 122
... PWL machines i = • " = · • To apply the closest - mode decision method , we need a training procedure to locate the modes or centers of high pattern density . Suppose that we have a PWL machine whose subsidiary discriminant functions ...
... PWL machines i = • " = · • To apply the closest - mode decision method , we need a training procedure to locate the modes or centers of high pattern density . Suppose that we have a PWL machine whose subsidiary discriminant functions ...
Sivu 123
... PWL machine one at a time from a training sequence . Let the initial weight vectors be selected arbitrarily . * We shall describe the adjustments to be made at the kth step . Suppose that the ( k + 1 ) st pattern in the training ...
... PWL machine one at a time from a training sequence . Let the initial weight vectors be selected arbitrarily . * We shall describe the adjustments to be made at the kth step . Suppose that the ( k + 1 ) st pattern in the training ...
Sivu 125
... PWL machine for the recognition of electrocardio- graph signals . It is known that there are several different " typical " elec- trocardiograph signals representing various types of normal and abnormal heart conditions . The results of ...
... PWL machine for the recognition of electrocardio- graph signals . It is known that there are several different " typical " elec- trocardiograph signals representing various types of normal and abnormal heart conditions . The results of ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
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adjusted apply assume bank called cells changes Chapter classifier cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern hyperplane pattern space pattern vector pattern-classifying 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 |