Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 58
... probability distributions for M ; and Σ ; it is meaning- less to speak of optimum estimates . derived from the training set as if they were the 58 PARAMETRIC TRAINING METHODS Learning the mean vector of normal patterns,
... probability distributions for M ; and Σ ; it is meaning- less to speak of optimum estimates . derived from the training set as if they were the 58 PARAMETRIC TRAINING METHODS Learning the mean vector of normal patterns,
Sivu 59
... mean vectors are assumed to be random variables . Suppose the pattern vectors belonging to category i are normal with known covariance matrix Σ ; and unknown mean vector . Thus , the d com- ponents of the mean vector are the only ...
... mean vectors are assumed to be random variables . Suppose the pattern vectors belonging to category i are normal with known covariance matrix Σ ; and unknown mean vector . Thus , the d com- ponents of the mean vector are the only ...
Sivu 61
... mean vector . We note the asymptotic results lim N → ∞ = UN ( X ) lim KN = 0 N → ∞ ( 3.51 ) Further insight into the process of learning the mean vector can be obtained by considering the special case where K = ( 1 / a ) , where a ...
... mean vector . We note the asymptotic results lim N → ∞ = UN ( X ) lim KN = 0 N → ∞ ( 3.51 ) Further insight into the process of learning the mean vector can be obtained by considering the special case where K = ( 1 / a ) , where a ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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adjusted apply assume bank belonging to category called changes Chapter cluster committee components consider consists contains correction corresponding decision surfaces define denote density depends derivation described Development discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed given implemented important initial layered machine linear dichotomies linear discriminant functions linear machine linearly separable measurements negative 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 space Stanford step subsidiary discriminant 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 |