Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Sivu 16
The training of a machine restricted to employ discriminant functions belonging to
a particular family can then be accomplished by adjusting the values of the
parameters . We shall often call these parameters weights . In this book we shall
be ...
The training of a machine restricted to employ discriminant functions belonging to
a particular family can then be accomplished by adjusting the values of the
parameters . We shall often call these parameters weights . In this book we shall
be ...
Sivu 44
We assume that the p ( Xll ) are known functions of a finite number of
characteristic parameters whose values we might not know a priori . For example
, we may know that the p ( X \ i ) , i = 1 , . . . , R , are normal probability - density
functions ...
We assume that the p ( Xll ) are known functions of a finite number of
characteristic parameters whose values we might not know a priori . For example
, we may know that the p ( X \ i ) , i = 1 , . . . , R , are normal probability - density
functions ...
Sivu 49
Note , for example , that the values of the a priori probabilities p ( 1 ) and 1 – p ( 1
) affect only the value of Wd41 . As category 1 becomes less probable a priori ,
Wd + 1 decreases . Such a decrease of Wd + 1 favors a category 2 response for ...
Note , for example , that the values of the a priori probabilities p ( 1 ) and 1 – p ( 1
) affect only the value of Wd41 . As category 1 becomes less probable a priori ,
Wd + 1 decreases . Such a decrease of Wd + 1 favors a category 2 response for ...
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Sisältö
Preface vii | 7 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
Tekijänoikeudet | |
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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 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 Stanford step Suppose theorem theory threshold training methods training procedure training sequence training subsets transformation values weight vectors zero
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 |
| Alkuperäisteoksen sijainti | Wisconsinin yliopisto - Madison |
| Digitoitu | 16. huhtikuu 2008 |
| Pituus | 137 sivua |
| Vie sitaatti | BiBTeX EndNote RefMan |