Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 20
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... belonging to category 1 and N2 patterns belonging to cate- gory 2. Reasonable estimates for X1 and X2 might then be the respective sample means ( centers of gravity ) of the patterns in each category . Once 2 Cluster point for category ...
... belonging to category 1 and N2 patterns belonging to cate- gory 2. Reasonable estimates for X1 and X2 might then be the respective sample means ( centers of gravity ) of the patterns in each category . Once 2 Cluster point for category ...
Sivu 52
... belonging to category 1 ; another might contain patterns belonging to category 2 , etc. For each category a cluster of pattern points exists that is more or less tightly grouped . The center of each cluster could be considered a ...
... belonging to category 1 ; another might contain patterns belonging to category 2 , etc. For each category a cluster of pattern points exists that is more or less tightly grouped . The center of each cluster could be considered a ...
Sivu 75
... belonging to more than two categories was defined in Chapter 2. It consists of R linear discriminators and a maximum ... category i . We desire to train the linear machine by adjusting its weight vectors so that it responds . correctly to ...
... belonging to more than two categories was defined in Chapter 2. It consists of R linear discriminators and a maximum ... category i . We desire to train the linear machine by adjusting its weight vectors so that it responds . correctly to ...
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 |