Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 21
Sivu 96
... layers have as their inputs the outputs of the : 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 : 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 104
... layer until the TLU in the last or Nth layer transforms the set g ( N - 1 ) of image points into the two vertices of a one - dimensional cube . These two vertices represent the two possible responses of the layered machine . Thus the ...
... layer until the TLU in the last or Nth layer transforms the set g ( N - 1 ) of image points into the two vertices of a one - dimensional cube . These two vertices represent the two possible responses of the layered machine . Thus the ...
Sivu 110
... machine for R = 2. Suppose the first layer has P TLUS . Let the binary output of the ith TLU in the first layer be denoted by u , and let the weight vector corresponding to this TLU be denoted by W ... layered machines. 110 LAYERED MACHINES.
... machine for R = 2. Suppose the first layer has P TLUS . Let the binary output of the ith TLU in the first layer be denoted by u , and let the weight vector corresponding to this TLU be denoted by W ... layered machines. 110 LAYERED MACHINES.
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
11 | 30 |
PARAMETRIC TRAINING METHODS | 43 |
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adjusted apply assume bank called cells changes Chapter classifier cluster column committee machine components Computer 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 terns theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors Y₁ zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |