Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Sivu 4
If d can be very large , we might not need to exercise much care in the selection
of measurements because it is likely that most of the important ones can be
included . But in the more usual and practical cases , in which ( for economic
reasons ) ...
If d can be very large , we might not need to exercise much care in the selection
of measurements because it is likely that most of the important ones can be
included . But in the more usual and practical cases , in which ( for economic
reasons ) ...
Sivu 8
For this reason the threshold element assumes an important role in pattern -
classifying machines . We shall use the block diagram of Fig . 1 . 5 as a basic
model of a two - category pattern classifier , which we call a pattern dichotomizer .
For this reason the threshold element assumes an important role in pattern -
classifying machines . We shall use the block diagram of Fig . 1 . 5 as a basic
model of a two - category pattern classifier , which we call a pattern dichotomizer .
Sivu 28
We shall see in Chapter 3 an important application of quadric surfaces . 2 : 10
Implementation of quadric discriminant functions There are two important
methods of implementing quadric discriminant functions . One is suggested by Eq
. ( 2 .
We shall see in Chapter 3 an important application of quadric surfaces . 2 : 10
Implementation of quadric discriminant functions There are two important
methods of implementing quadric discriminant functions . One is suggested by Eq
. ( 2 .
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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 |