Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 43
Sivu 16
... machines ; because all plane motion in machines is equivalent to either continuous or instantaneous rotation about some point . To find the relation of linear velocity of two points in a machine member , therefore , it is only necessary ...
... machines ; because all plane motion in machines is equivalent to either continuous or instantaneous rotation about some point . To find the relation of linear velocity of two points in a machine member , therefore , it is only necessary ...
Sivu 75
... MACHINE , LINEAR TYPE , HORIZONTAL MEASURING MACHINE , LINEAR TYPE , HORIZONTAL MEASURING MACHINE , LINEAR TYPE , HORIZONTAL MEASURING MACHINE , LINEAR TYPE , HORIZONTAL MEASURING MACHINE , LINEAR TYPE , HORIZONTAL MEASURING MACHINE ...
... MACHINE , LINEAR TYPE , HORIZONTAL MEASURING MACHINE , LINEAR TYPE , HORIZONTAL MEASURING MACHINE , LINEAR TYPE , HORIZONTAL MEASURING MACHINE , LINEAR TYPE , HORIZONTAL MEASURING MACHINE , LINEAR TYPE , HORIZONTAL MEASURING MACHINE ...
Sivu 485
... LIBLINEAR: A library for large linear classification. Journal of Machine Learning Research, 9, pp. 1871–1874, 2008. http://www.csie.ntu.edu.tw/∼cjlin/liblinear/ R. Fisher. The use of multiple measurements in taxonomic problems. Annals ...
... LIBLINEAR: A library for large linear classification. Journal of Machine Learning Research, 9, pp. 1871–1874, 2008. http://www.csie.ntu.edu.tw/∼cjlin/liblinear/ R. Fisher. The use of multiple measurements in taxonomic problems. Annals ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
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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 |