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SIAMSC
2011
177views more  SIAMSC 2011»
14 years 6 months ago
Computing f(A)b via Least Squares Polynomial Approximations
Given a certain function f, various methods have been proposed in the past for addressing the important problem of computing the matrix-vector product f(A)b without explicitly comp...
Jie Chen, Mihai Anitescu, Yousef Saad
PRL
2008
124views more  PRL 2008»
14 years 11 months ago
Matrix-pattern-oriented least squares support vector classifier with AdaBoost
: Matrix-pattern-oriented Least Squares Support Vector Classifier (MatLSSVC) can directly classify matrix patterns and has a superior classification performance than its vector ver...
Zhe Wang, Songcan Chen
NECO
2011
14 years 6 months ago
Least Squares Estimation Without Priors or Supervision
Selection of an optimal estimator typically relies on either supervised training samples (pairs of measurements and their associated true values), or a prior probability model for...
Martin Raphan, Eero P. Simoncelli
128
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ICML
1999
IEEE
16 years 17 days ago
Least-Squares Temporal Difference Learning
Excerpted from: Boyan, Justin. Learning Evaluation Functions for Global Optimization. Ph.D. thesis, Carnegie Mellon University, August 1998. (Available as Technical Report CMU-CS-...
Justin A. Boyan
SIAMSC
2011
142views more  SIAMSC 2011»
14 years 6 months ago
Efficiency Based Adaptive Local Refinement for First-Order System Least-Squares Formulations
Abstract. In this paper, we propose new adaptive local refinement (ALR) strategies for firstorder system least-squares (FOSLS) finite element in conjunction with algebraic multi...
J. H. Adler, Thomas A. Manteuffel, Stephen F. McCo...