Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 16
Sivu 6
... regions which we shall call decision regions . The ith region R ; is the set of points which map into the ith cate- gory number . For convenience , we shall arbitrarily assume that patterns which lie on decision surfaces do not belong ...
... regions which we shall call decision regions . The ith region R ; is the set of points which map into the ith cate- gory number . For convenience , we shall arbitrarily assume that patterns which lie on decision surfaces do not belong ...
Sivu 19
... regions and surfaces resulting from linear discriminant functions S 13 R3 R2 R2 S 12 FIGURE 2.3 S 23 Decision regions for a minimum - distance classifier with respect to the points P1 , P2 , and P3 In many cases some of the hyperplanes ...
... regions and surfaces resulting from linear discriminant functions S 13 R3 R2 R2 S 12 FIGURE 2.3 S 23 Decision regions for a minimum - distance classifier with respect to the points P1 , P2 , and P3 In many cases some of the hyperplanes ...
Sivu 20
... region lies entirely within the region ) . It will be left as an exercise for the reader to verify that the decision regions of a linear machine are always convex . 2.5 Linear classifications of patterns • , XN } , Suppose we have a ...
... region lies entirely within the region ) . It will be left as an exercise for the reader to verify that the decision regions of a linear machine are always convex . 2.5 Linear classifications of patterns • , XN } , Suppose we have a ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
2 muita osia ei näytetty
Muita painoksia - Näytä kaikki
Yleiset termit ja lausekkeet
adjusted apply assume bank belonging to category called changes Chapter cluster committee components consider consists contains correction corresponding 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 discriminant functions linear machine linearly separable 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 space Stanford step subsidiary discriminant Suppose theorem theory threshold training methods training procedure training sequence training subsets transformation values weight vectors zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |