Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 95
... ence have already been obtained . We shall limit our discussion primarily to the case R = 2. A layered machine is a network of TLUS organized in layers ( see Fig . 6-2 ) . Each TLU 95 LAYERED MACHINES 7 Layered networks of TLUs,
... ence have already been obtained . We shall limit our discussion primarily to the case R = 2. A layered machine is a network of TLUS organized in layers ( see Fig . 6-2 ) . Each TLU 95 LAYERED MACHINES 7 Layered networks of TLUs,
Sivu 96
... layers have as their inputs the outputs of the x2 X xd TLUS- Response Pattern FIGURE 6.1 A network of TLUS TLUS in the preceding layer only . The output of the single TLU in the final layer is the response of the machine . Layered machines ...
... layers have as their inputs the outputs of the x2 X xd TLUS- Response Pattern FIGURE 6.1 A network of TLUS TLUS in the preceding layer only . The output of the single TLU in the final layer is the response of the machine . Layered machines ...
Sivu 112
... layered machine is a piecewise linear machine . · A layered machine with P TLUs in the first layer has a total of 2o linear subsidiary discriminant functions ; these are divided into two classes ( corresponding to category 1 and ...
... layered machine is a piecewise linear machine . · A layered machine with P TLUs in the first layer has a total of 2o linear subsidiary discriminant functions ; these are divided into two classes ( corresponding to category 1 and ...
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 |