Learning Machines: Foundations of Trainable Pattern-classifying Systems |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 18
Sivu 32
2:13 The number of linear dichotomies of N points of d dimensions 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 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 67
Therefore , each region in weight space corresponds to a different linear dichotomy of the N patterns ... of dichotomies of N d - dimensional patterns implementable by a TLU , that is , the number of linear dichotomies of N patterns .
Therefore , each region in weight space corresponds to a different linear dichotomy of the N patterns ... of dichotomies of N d - dimensional patterns implementable by a TLU , that is , the number of linear dichotomies of N patterns .
Mitä ihmiset sanovat - Kirjoita arvostelu
Yhtään arvostelua ei löytynyt.
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 covariance 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 machine linearly separable matrix 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 regions respect response rule sample mean selection separable shown side solution space Stanford step 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 |
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 |