Results 1  10
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155,611
From PACBayes Bounds to Quadratic Programs for Majority Votes
"... We propose to construct a weighted majority vote on a set of basis functions by minimizing a risk bound (called the Cbound) that depends on the first two moments of the margin of the Qconvex combination realized on the data. This bound minimization algorithm turns out to be a quadratic program tha ..."
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Cited by 16 (6 self)
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We propose to construct a weighted majority vote on a set of basis functions by minimizing a risk bound (called the Cbound) that depends on the first two moments of the margin of the Qconvex combination realized on the data. This bound minimization algorithm turns out to be a quadratic program
From PACBayes bounds to KL regularization
 Advances in Neural Information Processing Systems 22
, 2009
"... We show that convex KLregularized objective functions are obtained from a PACBayes risk bound when using convex loss functions for the stochastic Gibbs classifier that upperbound the standard zeroone loss used for the weighted majority vote. By restricting ourselves to a class of posteriors, tha ..."
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Cited by 9 (3 self)
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We show that convex KLregularized objective functions are obtained from a PACBayes risk bound when using convex loss functions for the stochastic Gibbs classifier that upperbound the standard zeroone loss used for the weighted majority vote. By restricting ourselves to a class of posteriors
An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants
 MACHINE LEARNING
, 1999
"... Methods for voting classification algorithms, such as Bagging and AdaBoost, have been shown to be very successful in improving the accuracy of certain classifiers for artificial and realworld datasets. We review these algorithms and describe a large empirical study comparing several variants in co ..."
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Cited by 695 (2 self)
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for NaiveBayes,
which was very stable. We observed that Arcx4 behaves differently
than AdaBoost if reweighting is used instead of resampling,
indicating a fundamental difference. Voting variants, some of which
are introduced in this paper, include: pruning versus no pruning,
use
Boosting a Weak Learning Algorithm By Majority
, 1995
"... We present an algorithm for improving the accuracy of algorithms for learning binary concepts. The improvement is achieved by combining a large number of hypotheses, each of which is generated by training the given learning algorithm on a different set of examples. Our algorithm is based on ideas pr ..."
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Cited by 516 (15 self)
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presented by Schapire in his paper "The strength of weak learnability", and represents an improvement over his results. The analysis of our algorithm provides general upper bounds on the resources required for learning in Valiant's polynomial PAC learning framework, which are the best general
Learning the Kernel Matrix with SemiDefinite Programming
, 2002
"... Kernelbased learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points. The embedding is performed implicitly, by specifying the inner products between each pair of points in the embedding space. This information ..."
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Cited by 780 (22 self)
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problems in machine learning. In this paper we show how the kernel matrix can be learned from data via semidefinite programming (SDP) techniques. When applied
PACBayes bounds for the risk of the majority vote and the variance of the Gibbs classifier
 In Neural Information Processing Systems (NIPS
, 2006
"... We propose new PACBayes bounds for the risk of the weighted majority vote that depend on the mean and variance of the error of its associated Gibbs classifier. We show that these bounds can be smaller than the risk of the Gibbs classifier and can be arbitrarily close to zero even if the risk of the ..."
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Cited by 17 (3 self)
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We propose new PACBayes bounds for the risk of the weighted majority vote that depend on the mean and variance of the error of its associated Gibbs classifier. We show that these bounds can be smaller than the risk of the Gibbs classifier and can be arbitrarily close to zero even if the risk
Just Relax: Convex Programming Methods for Identifying Sparse Signals in Noise
, 2006
"... This paper studies a difficult and fundamental problem that arises throughout electrical engineering, applied mathematics, and statistics. Suppose that one forms a short linear combination of elementary signals drawn from a large, fixed collection. Given an observation of the linear combination that ..."
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Cited by 496 (2 self)
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This paper studies a difficult and fundamental problem that arises throughout electrical engineering, applied mathematics, and statistics. Suppose that one forms a short linear combination of elementary signals drawn from a large, fixed collection. Given an observation of the linear combination
SNOPT: An SQP Algorithm For LargeScale Constrained Optimization
, 2002
"... Sequential quadratic programming (SQP) methods have proved highly effective for solving constrained optimization problems with smooth nonlinear functions in the objective and constraints. Here we consider problems with general inequality constraints (linear and nonlinear). We assume that first deriv ..."
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Cited by 582 (23 self)
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Sequential quadratic programming (SQP) methods have proved highly effective for solving constrained optimization problems with smooth nonlinear functions in the objective and constraints. Here we consider problems with general inequality constraints (linear and nonlinear). We assume that first
Results 1  10
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155,611