Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 38
Sivu 99
... training procedure for committee machines Suppose that we have training pattern subsets y1 and Y2 , comprising the training set Y , and we wish to find a committee machine of size P to separate these subsets . To accomplish this , we ...
... training procedure for committee machines Suppose that we have training pattern subsets y1 and Y2 , comprising the training set Y , and we wish to find a committee machine of size P to separate these subsets . To accomplish this , we ...
Sivu 119
... training methods . Suppose that we decided to use an error - correction training procedure to train a single TLU . Even though a TLU is capable of implementing the optimum decision surface , an error - correction procedure could never ...
... training methods . Suppose that we decided to use an error - correction training procedure to train a single TLU . Even though a TLU is capable of implementing the optimum decision surface , an error - correction procedure could never ...
Sivu 122
... training set . In the next section we shall present a candidate training method . 7.6 Mode - seeking and related training methods for PWL machines 9 To apply the closest - mode decision method , we need a training procedure to locate ...
... training set . In the next section we shall present a candidate training method . 7.6 Mode - seeking and related training methods for PWL machines 9 To apply the closest - mode decision method , we need a training procedure to locate ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
11 | 30 |
PARAMETRIC TRAINING METHODS | 43 |
Tekijänoikeudet | |
6 muita osia ei näytetty
Muita painoksia - Näytä kaikki
Yleiset termit ja lausekkeet
adjusted apply assume bank called cells changes Chapter classifier cluster column committee machine components Computer consider consists contains correction corresponding covariance decision surfaces define denote density depends described discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern hyperplane pattern space pattern vector pattern-classifying piecewise linear plane points positive presented probability problem properties PWL machine quadric regions respect response rule selection separable sequence side solution space Stanford step subsidiary discriminant Suppose terns theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors Y₁ zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |