Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Tulokset 1 - 3 kokonaismäärästä 42
Sivu 16
The training of a machine restricted to employ discriminant functions belonging to
a particular family can then be accomplished by ... An important special case of a
linear machine is a minimum - distance classifier with respect to points .
The training of a machine restricted to employ discriminant functions belonging to
a particular family can then be accomplished by ... An important special case of a
linear machine is a minimum - distance classifier with respect to points .
Sivu 20
In the special case in which the linear machine is a minimum - distance classifier
, the surface Sij is the hyperplane which is the perpendicular bisector of the line
segment joining the points Pi and P ; . Figure 2 : 3 shows the decision regions ...
In the special case in which the linear machine is a minimum - distance classifier
, the surface Sij is the hyperplane which is the perpendicular bisector of the line
segment joining the points Pi and P ; . Figure 2 : 3 shows the decision regions ...
Sivu 134
Decision regions , 6 convexity of , 20 of a linear machine , 19 , 20 of a piecewise
linear machine , 26 , 27 Error - correction training methods , 69 , 80 for committee
machines , 99 convergence of , 71 , 72 , 75 disadvantages of , 118 graphical ...
Decision regions , 6 convexity of , 20 of a linear machine , 19 , 20 of a piecewise
linear machine , 26 , 27 Error - correction training methods , 69 , 80 for committee
machines , 99 convergence of , 71 , 72 , 75 disadvantages of , 118 graphical ...
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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 |