Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 11
Sivu 88
... reduced training se- quence . The resulting weight - vector sequences Sŵ ,, Sŵ ,, . . . , SŵR gener- ated from Sŷ by the above rule will be called reduced weight - vector se- quences . We can now state the following theorem . Theorem ...
... reduced training se- quence . The resulting weight - vector sequences Sŵ ,, Sŵ ,, . . . , SŵR gener- ated from Sŷ by the above rule will be called reduced weight - vector se- quences . We can now state the following theorem . Theorem ...
Sivu 89
... reduced training sequence Sy and the reduced weight - vector sequences Sŵ ,, ... , SŴR a corresponding sequence of vectors from the set Z. Let us denote this sequence of vectors from Z by the symbol Sz . Corresponding to the kth member ...
... reduced training sequence Sy and the reduced weight - vector sequences Sŵ ,, ... , SŴR a corresponding sequence of vectors from the set Z. Let us denote this sequence of vectors from Z by the symbol Sz . Corresponding to the kth member ...
Sivu 136
... training theorem , 87 rth - order polynomial functions , 30 , 38 Randall , 62 , 63 Rank of sample covariance matrix , 58 Rao , 12 , 13 Reduced training sequence , 82 Reduced weight - vector sequence , 82 Reduction of number 136 INDEX 65 79.
... training theorem , 87 rth - order polynomial functions , 30 , 38 Randall , 62 , 63 Rank of sample covariance matrix , 58 Rao , 12 , 13 Reduced training sequence , 82 Reduced weight - vector sequence , 82 Reduction of number 136 INDEX 65 79.
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
2 muita osia ei näytetty
Muita painoksia - Näytä kaikki
Yleiset termit ja lausekkeet
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 |