Minimization methods for non-differentiable functions
Minimization methods for non-differentiable functions
Proximity control in bundle methods for convex
Mathematical Programming: Series A and B
Parallel bundle-based decomposition for large-scale structured mathematical programming problems
Annals of Operations Research
Quadratic approximations in convex nondifferentiable optimization
SIAM Journal on Control and Optimization
The nature of statistical learning theory
The nature of statistical learning theory
A family of variable metric proximal methods
Mathematical Programming: Series A and B
Mathematical Programming: Series A and B
New variants of bundle methods
Mathematical Programming: Series A and B
A quasi-second-order proximal bundle algorithm.
Mathematical Programming: Series A and B
SIAM Review
Variable metric bundle methods: from conceptual to implementable forms
Mathematical Programming: Series A and B - Special issue on computational nonsmooth optimization
A preconditioning proximal Newton method for nondifferentiable convex optimization
Mathematical Programming: Series A and B - Special issue on computational nonsmooth optimization
Partitioning mathematical programs for parallel solution
Mathematical Programming: Series A and B
Advances in kernel methods: support vector learning
Advances in kernel methods: support vector learning
Semi-supervised support vector machines
Proceedings of the 1998 conference on Advances in neural information processing systems II
Globally convergent BFGS method for nonsmooth convex optimization
Journal of Optimization Theory and Applications
Efficiency of proximal bundle methods
Journal of Optimization Theory and Applications
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Quasi-Newton Bundle-Type Methods for Nondifferentiable Convex Optimization
SIAM Journal on Optimization
SIAM Journal on Optimization
On $\mathcalVU$-theory for Functions with Primal-Dual Gradient Structure
SIAM Journal on Optimization
A Globally and Superlinearly Convergent Algorithm for Nonsmooth Convex Minimization
SIAM Journal on Optimization
Practical Aspects of the Moreau--Yosida Regularization: Theoretical Preliminaries
SIAM Journal on Optimization
Polyhedral separability through successive LP
Journal of Optimization Theory and Applications
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
A Bundle Type Dual-Ascent Approach to Linear Multicommodity Min-Cost Flow Problems
INFORMS Journal on Computing
INFORMS Journal on Computing
Minimizing Nonconvex Nonsmooth Functions via Cutting Planes and Proximity Control
SIAM Journal on Optimization
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Smooth minimization of non-smooth functions
Mathematical Programming: Series A and B
A Robust Gradient Sampling Algorithm for Nonsmooth, Nonconvex Optimization
SIAM Journal on Optimization
A **-algorithm for convex minimization
Mathematical Programming: Series A and B
Trading convexity for scalability
ICML '06 Proceedings of the 23rd international conference on Machine learning
Computation of Minimum-Volume Covering Ellipsoids
Operations Research
The Interplay of Optimization and Machine Learning Research
The Journal of Machine Learning Research
Accurately learning from few examples with a polyhedral classifier
Computational Optimization and Applications
Nonsmooth Optimization Techniques for Semisupervised Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
A descent proximal level bundle method for convex nondifferentiable optimization
Operations Research Letters
Review: Supervised classification and mathematical optimization
Computers and Operations Research
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We review the role played by non-smooth optimization techniques in many recent applications in classification area. Starting from the classical concept of linear separability in binary classification, we recall the more general concepts of polyhedral, ellipsoidal and max-min separability. Finally we focus our attention on the support vector machine (SVM) approach and on the more recent transductive SVM technique.