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Orthogonal matching pursuit: Recursive function approximation with applications to wavelet decomposition
 in Conference Record of The TwentySeventh Asilomar Conference on Signals, Systems and Computers
, 1993
"... In this paper we describe a recursive algorithm to compute representations of functions with respect to nonorthogonal and possibly overcomplete dictionaries of elementary building blocks e.g. aiEne (wa.velet) frames. We propoeea modification to the Matching Pursuit algorithm of Mallat and Zhang (199 ..."
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Cited by 637 (1 self)
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recursively. where fk is the current approximation, and Rkf the current residual (error). Using initial values ofR0f = 1, fo = 0, and k = 1, the MP algorithm is comprised of the following steps,.,.41) Compute the innerproducts {(Rkf,z)}. (H) Find flki such that (III) Set, I(R*f,1:n 1+,)l asupl
Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification
 Psychological Methods
, 1998
"... This study evaluated the sensitivity of maximum likelihood (ML), generalized least squares (GLS), and asymptotic distributionfree (ADF)based fit indices to model misspecification, under conditions that varied sample size and distribution. The effect of violating assumptions of asymptotic robustn ..."
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Cited by 543 (0 self)
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robustness theory also was examined. Standardized rootmeansquare residual (SRMR) was the most sensitive index to models with misspecified factor covariance(s), and TuckerLewis Index (1973; TLI), Bollen's fit index (1989; BL89), relative noncentrality index (RNI), comparative fit index (CFI
Residual Algorithms: Reinforcement Learning with Function Approximation
 In Proceedings of the Twelfth International Conference on Machine Learning
, 1995
"... A number of reinforcement learning algorithms have been developed that are guaranteed to converge to the optimal solution when used with lookup tables. It is shown, however, that these algorithms can easily become unstable when implemented directly with a general functionapproximation system, such ..."
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Cited by 307 (6 self)
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, such as a sigmoidal multilayer perceptron, a radialbasisfunction system, a memorybased learning system, or even a linear functionapproximation system. A new class of algorithms, residual gradient algorithms, is proposed, which perform gradient descent on the mean squared Bellman residual, guaranteeing
Protein homology detection by HMMHMM comparison
 BIOINFORMATICS
, 2005
"... Motivation: Protein homology detection and sequence alignment are at the basis of protein structure prediction, function prediction, and evolution. Results: We have generalized the alignment of protein sequences with a profile hidden Markov model (HMM) to the case of pairwise alignment of profile H ..."
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Cited by 401 (8 self)
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_SIM for a rate of false positives of 10%. Approximately half of the improvement over the profile–profile comparison methods is attributable to the use of profile HMMs in place of simple profiles. Alignment quality: Higher sensitivity is mirrored by an increased alignment quality. HHsearch produced 1.2, 1
Cryptographic Limitations on Learning Boolean Formulae and Finite Automata
 PROCEEDINGS OF THE TWENTYFIRST ANNUAL ACM SYMPOSIUM ON THEORY OF COMPUTING
, 1989
"... In this paper we prove the intractability of learning several classes of Boolean functions in the distributionfree model (also called the Probably Approximately Correct or PAC model) of learning from examples. These results are representation independent, in that they hold regardless of the syntact ..."
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Cited by 347 (14 self)
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In this paper we prove the intractability of learning several classes of Boolean functions in the distributionfree model (also called the Probably Approximately Correct or PAC model) of learning from examples. These results are representation independent, in that they hold regardless
Efficient Rankone Residue Approximation Method for Graph Regularized Nonnegative Matrix Factorization
"... Abstract. Nonnegative matrix factorization (NMF) aims to decompose a given data matrix X into the product of two lowerrank nonnegative factor matrices UV T. Graph regularized NMF (GNMF) is a recently proposed NMF method that preserves the geometric structure of X during such decomposition. Althoug ..."
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. Although GNMF has been widely used in computer vision and data mining, its multiplicative update rule (MUR) based solver suffers from both slow convergence and nonstationarity problems. In this paper, we propose a new efficient GNMF solver called rankone residue approximation (RRA). Different from MUR
Estimation of effective interresidue contact energies from protein crystal structures: quasichemical approximation
 Macromolecules
, 1985
"... ABSTRACT: Effective interresidue contact energies for proteins in solution are estimated from the numbers of residueresidue contacts observed in crystal structures of globular proteins by means of the quasichemical approximation with an approximate treatment of the effects of chain connectivity. E ..."
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Cited by 269 (11 self)
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ABSTRACT: Effective interresidue contact energies for proteins in solution are estimated from the numbers of residueresidue contacts observed in crystal structures of globular proteins by means of the quasichemical approximation with an approximate treatment of the effects of chain connectivity
A Factor 2 Approximation Algorithm for the Generalized Steiner Network Problem
 COMBINATORICA
"... We present a factor 2 approximation algorithm for finding a minimumcost subgraph having at least a specified number of edges in each cut. This class of problems includes, among others, the generalized Steiner network problem, which is also known as the survivable network design problem. Our algorit ..."
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Cited by 266 (3 self)
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We present a factor 2 approximation algorithm for finding a minimumcost subgraph having at least a specified number of edges in each cut. This class of problems includes, among others, the generalized Steiner network problem, which is also known as the survivable network design problem. Our
Residueresidue potentials with a favorable contact pair term and an unfavorable high packing density term, for simulation and threading
, 1996
"... Attractive interresidue contact energies for proteins have been reevaluated with the same assumptions and approximations used originally by us in 1985, but with a significantly larger set of protein crystal structures. An additional repulsive packing energy term, operative at higher densities to p ..."
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Cited by 236 (12 self)
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Attractive interresidue contact energies for proteins have been reevaluated with the same assumptions and approximations used originally by us in 1985, but with a significantly larger set of protein crystal structures. An additional repulsive packing energy term, operative at higher densities
Stochastic Gradient Boosting
 Computational Statistics and Data Analysis
, 1999
"... Gradient boosting constructs additive regression models by sequentially fitting a simple parameterized function (base learner) to current "pseudo"residuals by leastsquares at each iteration. The pseudoresiduals are the gradient of the loss functional being minimized, with respect to ..."
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Cited by 285 (1 self)
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Gradient boosting constructs additive regression models by sequentially fitting a simple parameterized function (base learner) to current "pseudo"residuals by leastsquares at each iteration. The pseudoresiduals are the gradient of the loss functional being minimized, with respect
Results 1  10
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