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ACM International Conference Proceeding Series; Vol. 227: Proceedings of the 24th international conference on Machine learning; 20-24 June 2007, 2007, p.807-814

2007

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Pegasos: primal estimated sub-gradient solver for SVM: Optimization and Machine Learning

Mathematical programming, 2011, Vol.127 (1), p.3-30

2011

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SVM optimization: inverse dependence on training set size

Proceedings of the 25th international conference on Machine learning, 2008, p.928-935

2008

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Smooth ε-insensitive regression by loss symmetrization

Lecture notes in computer science, 2003, p.433-447

2003

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Theory and Practice of Support Vector Machines Optimization

Automatic Speech and Speaker Recognition, 2009, p.11-26

1, 2009

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Learnability beyond Uniform Convergence

Algorithmic Learning Theory, p.13-16

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Stochastic methods for l1 regularized loss minimization

Proceedings of the 26th Annual International Conference on Machine Learning, 2009, p.929-936

2009

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A Large Margin Algorithm for Forced Alignment

Automatic Speech and Speaker Recognition, 2009, p.51-68

1, 2009

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Accelerated proximal stochastic dual coordinate ascent for regularized loss minimization

Pegasos: primal estimated sub-gradient solver for SVM

THE FORGETRON : A KERNEL-BASED PERCEPTRON ON A BUDGET

On the equivalence of weak learnability and linear separability: new relaxations and efficient boosting algorithms

From average case complexity to improper learning complexity

Near-Optimal Algorithms for Online Matrix Prediction

Pegasos: primal estimated sub-gradient solver for SVM

Trading Accuracy for Sparsity in Optimization Problems with Sparsity Constraints

A primal-dual perspective of online learning algorithms

Online Learning of Noisy Data

LEARNING KERNEL-BASED HALFSPACES WITH THE 0-1 LOSS

A New Perspective on an Old Perceptron Algorithm

A Large Margin Algorithm for Speech-to-Phoneme and Music-to-Score Alignment

Managing extreme AI risks amid rapid progress

Individual Sequence Prediction Using Memory-Efficient Context Trees

On the equivalence of weak learnability and linear separability: new relaxations and efficient boosting algorithms: Special Issue on Learning Theory

Efficient projections onto the l1-ball for learning in high dimensions

Pegasos: Primal Estimated sub-GrAdient SOlver for SVM

Pegasos: primal estimated sub-gradient solver for SVM: Optimization and Machine Learning

SVM optimization: inverse dependence on training set size

Smooth ε-insensitive regression by loss symmetrization

Theory and Practice of Support Vector Machines Optimization

Learnability beyond Uniform Convergence

Stochastic methods for l1 regularized loss minimization

A Large Margin Algorithm for Forced Alignment

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