Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Sivu
A learning machine , broadly defined , is any device whose actions are
influenced by past experiences . ... The basic approach adopted in this book
involves the concept of discriminant functions that define the behavior of the
pattern ...
A learning machine , broadly defined , is any device whose actions are
influenced by past experiences . ... The basic approach adopted in this book
involves the concept of discriminant functions that define the behavior of the
pattern ...
Sivu 24
Let us define the Euclidean distance d ( X , Pi ) from an arbitrary point X to the
point set Pi by d ( X , P ; ) = min IX – P ; ( 0 ) | | ( 2 : 16 ) j = 1 , . . . , Li That is , the
distance between X and Pi is the smallest of the distances between X and each
point ...
Let us define the Euclidean distance d ( X , Pi ) from an arbitrary point X to the
point set Pi by d ( X , P ; ) = min IX – P ; ( 0 ) | | ( 2 : 16 ) j = 1 , . . . , Li That is , the
distance between X and Pi is the smallest of the distances between X and each
point ...
Sivu 47
As in Chapter 1 , we define the discriminant function , 9 ( X ) = 91 ( X ) – 92 ( X ) .
If g ( x ) > 0 , the machine places X in category 1 ; if g ( x ) < 0 , the machine
places X in category 2 . From Eq . ( 3 . 76 ) we can derive s p ( 1 ) ] g ( x ) = 512 ) [
1 – p ...
As in Chapter 1 , we define the discriminant function , 9 ( X ) = 91 ( X ) – 92 ( X ) .
If g ( x ) > 0 , the machine places X in category 1 ; if g ( x ) < 0 , the machine
places X in category 2 . From Eq . ( 3 . 76 ) we can derive s p ( 1 ) ] g ( x ) = 512 ) [
1 – p ...
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Sisältö
Preface vii | 1 |
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 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 |