Surrogate maximization/minimization algorithms and extensions

Surrogate maximization (or minimization) (SM) algorithms are a family of algorithms that can be regarded as a generalization of expectation-maximization (EM) algorithms. An SM algorithm aims at turning an otherwise intractable maximization problem into a tractable one by iterating two steps. The S-s...

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Veröffentlicht in:	Machine learning Jg. 69; H. 1; S. 1 - 33
Hauptverfasser:	Zhang, Zhihua, Kwok, James T., Yeung, Dit-Yan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Dordrecht Springer 01.10.2007 Springer Nature B.V
Schlagworte:	Applied sciences Artificial intelligence Computer science; control theory; systems Exact sciences and technology Studies Gradient Loglinear model Minimization Regression analysis Jensen inequality Aggregate model Logistic regression Surrogate function,Convexity,Logistic regression,AdaBoost Supervised learning Regression model Classification Quadratic approximation Regression function Convexity Objective function EM algorithm Artificial intelligence Log-linear model Step function
ISSN:	0885-6125, 1573-0565
Online-Zugang:	Volltext
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