Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Sivu 7
discriminant functions . Of course , the location and form of the decision surfaces
do not uniquely specify the discriminant functions . For one thing , the same
arbitrary constant can be added to each discriminant function without altering the
...
discriminant functions . Of course , the location and form of the decision surfaces
do not uniquely specify the discriminant functions . For one thing , the same
arbitrary constant can be added to each discriminant function without altering the
...
Sivu 16
A particular function belonging to this family can be selected by choosing the
appropriate values of the parameters . The training of a machine restricted to
employ discriminant functions belonging to a particular family can then be ...
A particular function belonging to this family can be selected by choosing the
appropriate values of the parameters . The training of a machine restricted to
employ discriminant functions belonging to a particular family can then be ...
Sivu 112
and for any pattern X producing a U for which H ( U ) = - 1 max , { 9 ; 6 ( X ) } <
max , { 92 % * ( X ) } ( 6 : 15 ) i = 1 , . . . , L " i = 1 , . . . , 2P - L max Inequalities ( 6 .
14 ) and ( 6 . 15 ) lead us to define the discriminant functions 91 ( X ) = max , { 9 "
( x ) ...
and for any pattern X producing a U for which H ( U ) = - 1 max , { 9 ; 6 ( X ) } <
max , { 92 % * ( X ) } ( 6 : 15 ) i = 1 , . . . , L " i = 1 , . . . , 2P - L max Inequalities ( 6 .
14 ) and ( 6 . 15 ) lead us to define the discriminant functions 91 ( X ) = max , { 9 "
( x ) ...
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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 Development discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed gi(X given implemented important initial layered machine linear dichotomies linear machine linearly separable matrix measurements 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 solution space specific 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 |