Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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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
dichotomies is ...
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
dichotomies is ...
Sivu 33
We wish to find out by how much this number of linear dichotomies is increased if
the set X ' is enlarged to ly ( a ) Points in general position ( 6 ) Three points
collinear FIGURE 2 . 9 Linear partitions of four points in E2 include one more
point Xy ...
We wish to find out by how much this number of linear dichotomies is increased if
the set X ' is enlarged to ly ( a ) Points in general position ( 6 ) Three points
collinear FIGURE 2 . 9 Linear partitions of four points in E2 include one more
point Xy ...
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 . For the members of X in general ...
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 . For the members of X in general ...
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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 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 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 solution 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 |