Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Tulokset 1 - 3 kokonaismäärästä 17
Sivu 20
Stated another way , a classification of X is linear and the subsets X1 , X2 , . . . , Xr
are linearly separable if and only if linear discriminant functions 91 , 92 , . . . , OR
exist such that g : ( X ) > gi ( X ) j = 1 , . . . , R , j * for all X in Xi for all i = 1 , . . . , R ...
Stated another way , a classification of X is linear and the subsets X1 , X2 , . . . , Xr
are linearly separable if and only if linear discriminant functions 91 , 92 , . . . , OR
exist such that g : ( X ) > gi ( X ) j = 1 , . . . , R , j * for all X in Xi for all i = 1 , . . . , R ...
Sivu 21
Because the decision regions of a linear machine are convex , it is easy to show
that if the subsets X1 , X2 , . . . , Xr are linearly separable , then each pair of
subsets Xi , X ; , i , j = 1 , . . . , R , i * j , is also linearly separable . That is , if X1 , X2
, . . .
Because the decision regions of a linear machine are convex , it is easy to show
that if the subsets X1 , X2 , . . . , Xr are linearly separable , then each pair of
subsets Xi , X ; , i , j = 1 , . . . , R , i * j , is also linearly separable . That is , if X1 , X2
, . . .
Sivu 107
When these conditions are met , I1 ( 1 ) and I ( 1 ) are guaranteed to be linearly
separable , and thus a two - layer ... 6 : 6 A sufficient condition for image - space
linear separability Before stating and proving the sufficient condition it will be ...
When these conditions are met , I1 ( 1 ) and I ( 1 ) are guaranteed to be linearly
separable , and thus a two - layer ... 6 : 6 A sufficient condition for image - space
linear separability Before stating and proving the sufficient condition it will be ...
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Sisältö
Preface vii | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
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 Development 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 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 |
| Pituus | 137 sivua |
| Vie sitaatti | BiBTeX EndNote RefMan |