Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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The parametric training method for the design of р discriminant functions then consists of three steps : 1. ... to be the true values of the parameters and are used in the expressions for the discriminant functions developed in step 1 .
The parametric training method for the design of р discriminant functions then consists of three steps : 1. ... to be the true values of the parameters and are used in the expressions for the discriminant functions developed in step 1 .
Sivu 84
Other than the fact that a bound on the number of steps exists , thus proving the theorem , the bound itself is not very useful in estimating how many steps will be required in a given situation , since it depends on knowledge of a ...
Other than the fact that a bound on the number of steps exists , thus proving the theorem , the bound itself is not very useful in estimating how many steps will be required in a given situation , since it depends on knowledge of a ...
Sivu 86
We now compute the decrease in squared distance to W , dx + 1 , effected by the kth step dati W – W ? – W – W4 + 1 / 2 Wxl2 ( 5.24 ) Let Yk be the kth pattern vector in the reduced training sequence Sp . w.y , = { ( m + b ) M = w • Y ...
We now compute the decrease in squared distance to W , dx + 1 , effected by the kth step dati W – W ? – W – W4 + 1 / 2 Wxl2 ( 5.24 ) Let Yk be the kth pattern vector in the reduced training sequence Sp . w.y , = { ( m + b ) M = w • Y ...
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Sisältö
I | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
APPENDIX | 127 |
Tekijänoikeudet | |
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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 |