Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Sivu 10
Suppose the training set consisted of N1 patterns belonging to category 1 and N2
patterns belonging to category 2 . Reasonable estimates for X , and X , might
then be the respective sample means ( centers of gravity ) of the patterns in each
...
Suppose the training set consisted of N1 patterns belonging to category 1 and N2
patterns belonging to category 2 . Reasonable estimates for X , and X , might
then be the respective sample means ( centers of gravity ) of the patterns in each
...
Sivu 75
The subset Yi contains all training patterns in y belonging to category i . We
desire to train the linear machine by adjusting its weight vectors so that it
responds correctly to every pattern in Y . A response to a pattern in category i is
correct only if ...
The subset Yi contains all training patterns in y belonging to category i . We
desire to train the linear machine by adjusting its weight vectors so that it
responds correctly to every pattern in Y . A response to a pattern in category i is
correct only if ...
Sivu 121
Suppose the modes for the various categories , as established by a training
procedure , are given by the points P ; ( ) for i = 1 , . . . , R and j = 1 , . . . , Li . That
is , there are L , typical patterns belonging to category 1 , L2 belonging to
category 2 ...
Suppose the modes for the various categories , as established by a training
procedure , are given by the points P ; ( ) for i = 1 , . . . , R and j = 1 , . . . , Li . That
is , there are L , typical patterns belonging to category 1 , L2 belonging to
category 2 ...
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adjusted apply assume bank belonging to category called changes Chapter cluster committee components consider consists contains correction corresponding covariance decision surfaces define denote density depends derivation Development discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed gi(X given implemented important initial layered machine linear dichotomies linear machine linearly separable matrix measurements networks normal Note optimum origin parameters partition pattern classifier pattern hyperplane pattern space pattern vector piecewise linear plane points positive presented probability problem proof properties proved PWL machine quadric reduced regions respect response rule sample mean selection separable shown side solution space specific Stanford step Suppose theorem theory threshold training methods training procedure training sequence training subsets transformation values weight vectors zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |