Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 28
Sivu 58
... mean ( or center of gravity ) of the ith category , and ( E ) ; is called the sample covariance matrix of the ith category . The ( X ) ; and ( ) ; are reasonable * estimates of M , and 2 , respectively . The use of these estimates to ...
... mean ( or center of gravity ) of the ith category , and ( E ) ; is called the sample covariance matrix of the ith category . The ( X ) ; and ( ) ; are reasonable * estimates of M , and 2 , respectively . The use of these estimates to ...
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 | |
3 muita osia ei näytetty
Muita painoksia - Näytä kaikki
Yleiset termit ja lausekkeet
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 |