Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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... development thus represents an evolution in development concerns. Initially there was an exclusive focus on growth and its socioeconomic benefits. The human development paradigm then integrated political and socioeconomic concerns ...
... development thus represents an evolution in development concerns. Initially there was an exclusive focus on growth and its socioeconomic benefits. The human development paradigm then integrated political and socioeconomic concerns ...
Sivu 22
... development paradigm At the beginning of the 1980s, two research programs converged to give rise to the formation of the ... development potential 22 The notion of endogenous development The formation of the endogenous development paradigm.
... development paradigm At the beginning of the 1980s, two research programs converged to give rise to the formation of the ... development potential 22 The notion of endogenous development The formation of the endogenous development paradigm.
Sivu 229
... development and NGOs: an overview', Institutional Development, IV, 1:3–19. Tandon, Y. (1996) 'An African perspective', in D. Sogge, K.Biekart and J.Saxby (eds) Compassion and Calculation: the Business of Private Foreign Aid, London ...
... development and NGOs: an overview', Institutional Development, IV, 1:3–19. Tandon, Y. (1996) 'An African perspective', in D. Sogge, K.Biekart and J.Saxby (eds) Compassion and Calculation: the Business of Private Foreign Aid, London ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
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adjusted apply assume bank called cells changes Chapter classifier cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern hyperplane pattern space pattern vector pattern-classifying piecewise linear plane points positive presented probability problem properties PWL machine quadric regions respect response rule selection separable sequence side solution space step subsidiary discriminant Suppose terns theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors X1 and X2 Y₁ zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |