Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Tulokset 1 - 3 kokonaismäärästä 18
Sivu 32
2:13 The number of linear dichotomies of N points of d dimensions = Z We shall begin by calculating the number of dichotomies of N patterns achievable by a linear discriminant function ( i.e. , a TLU ) . Recall that each of these ...
2:13 The number of linear dichotomies of N points of d dimensions = Z We shall begin by calculating the number of dichotomies of N patterns achievable by a linear discriminant function ( i.e. , a TLU ) . Recall that each of these ...
Sivu 33
Assume that we have a set X ' of N – 1 points X1 , X2 , . . . , Xn - 1 in general position in Ed . There are L ( N – 1 , d ) linear dichotomies of X ' . We wish to find out by how much this number of linear dichotomies is increased if ...
Assume that we have a set X ' of N – 1 points X1 , X2 , . . . , Xn - 1 in general position in Ed . There are L ( N – 1 , d ) linear dichotomies of X ' . We wish to find out by how much this number of linear dichotomies is increased if ...
Sivu 37
Since any linear dichotomy of F in space corresponds to a $ dichotomy of X in the pattern space , $ ( N , d ) is equal to L ( N , M ) , which is the number of linear dichotomies of the set F of N points in space .
Since any linear dichotomy of F in space corresponds to a $ dichotomy of X in the pattern space , $ ( N , d ) is equal to L ( N , M ) , which is the number of linear dichotomies of the set F of N points in space .
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adjusted apply assume bank belonging to category called changes Chapter classifier cluster committee components consider consists contains correction corresponding decision surfaces define denote density depends derivation described discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed gi(X given illustrated implemented important initial known layered machine linear dichotomies linear machine linearly separable negative 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 selected separable shown side space specific Stanford step Suppose theorem theory threshold training methods training patterns 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 |
Kirjaluettelon tiedot
| Otsikko | Learning Machines: Foundations of Trainable Pattern-classifying Systems McGraw-Hill series in systems science |
| Kirjoittaja | Nils J. Nilsson |
| Painos | kuvitettu |
| Kustantaja | McGraw-Hill, 1965 |
| ISBN | 0070465703, 9780070465701 |
| Pituus | 137 sivua |
| Vie sitaatti | BiBTeX EndNote RefMan |