Fast approximation algorithms for fractional packing and covering problems
SFCS '91 Proceedings of the 32nd annual symposium on Foundations of computer science
Elements of information theory
Elements of information theory
Learning linear threshold functions in the presence of classification noise
COLT '94 Proceedings of the seventh annual conference on Computational learning theory
Randomized algorithms
Randomized Distributed Edge Coloring via an Extension of the Chernoff--Hoeffding Bounds
SIAM Journal on Computing
On PAC learning using Winnow, Perceptron, and a Perceptron-like algorithm
COLT '99 Proceedings of the twelfth annual conference on Computational learning theory
A simple polynomial-time rescaling algorithm for solving linear programs
STOC '04 Proceedings of the thirty-sixth annual ACM symposium on Theory of computing
Logarithmic regret algorithms for online convex optimization
Machine Learning
Beating Simplex for Fractional Packing and Covering Linear Programs
FOCS '07 Proceedings of the 48th Annual IEEE Symposium on Foundations of Computer Science
Coresets, sparse greedy approximation, and the Frank-Wolfe algorithm
Proceedings of the nineteenth annual ACM-SIAM symposium on Discrete algorithms
1-pass relative-error Lp-sampling with applications
SODA '10 Proceedings of the twenty-first annual ACM-SIAM symposium on Discrete Algorithms
On the generalization ability of on-line learning algorithms
IEEE Transactions on Information Theory
A sublinear-time randomized approximation algorithm for matrix games
Operations Research Letters
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In this article we describe and analyze sublinear-time approximation algorithms for some optimization problems arising in machine learning, such as training linear classifiers and finding minimum enclosing balls. Our algorithms can be extended to some kernelized versions of these problems, such as SVDD, hard margin SVM, and L2-SVM, for which sublinear-time algorithms were not known before. These new algorithms use a combination of a novel sampling techniques and a new multiplicative update algorithm. We give lower bounds which show the running times of many of our algorithms to be nearly best possible in the unit-cost RAM model.