Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 49
... probability distributions . As an example , consider the case of N successive Bernoulli trials where the probability of success is some number p . We assume that the value of p is unknown , but that it is governed by a uniform ...
... probability distributions . As an example , consider the case of N successive Bernoulli trials where the probability of success is some number p . We assume that the value of p is unknown , but that it is governed by a uniform ...
Sivu 50
... probability - density function In the example of Sec . 3.5 , we assumed that the pattern components were statistically independent , binary , random variables . Such an assumption permitted a straightforward calculation of the ...
... probability - density function In the example of Sec . 3.5 , we assumed that the pattern components were statistically independent , binary , random variables . Such an assumption permitted a straightforward calculation of the ...
Sivu 118
... probability of error . This is so because the underlying probability distributions may be sufficiently overlapping that the optimum decision surfaces do not perfectly separate the training subsets . Consider , for example , the ...
... probability of error . This is so because the underlying probability distributions may be sufficiently overlapping that the optimum decision surfaces do not perfectly separate the training subsets . Consider , for example , the ...
Sisältö
Preface vii | 11 |
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 important 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 |