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- Glenn Fung, Olvi L. Mangasarian
- KDD
- 2001

Instead of a standard support vector machine (SVM) that classifies points by assigning them to one of two disjoint half-spaces, points are classified by assigning them to the closest of two parallelâ€¦ (More)

- Glenn Fung, Olvi L. Mangasarian
- Comp. Opt. and Appl.
- 2004

A fast Newton method, that suppresses input space features, is proposed for a linear programming formulation of support vector machine classifiers. The proposed stand-alone method can handleâ€¦ (More)

- Mark W. Schmidt, Glenn Fung, RÃ³mer Rosales
- ECML
- 2007

L1 regularization is effective for feature selection, but the resulting optimization is challenging due to the non-differentiability of the 1-norm. In this paper we compare state-of-the-artâ€¦ (More)

- Glenn Fung, Olvi L. Mangasarian
- Machine Learning
- 2005

Given a dataset, each element of which labeled by one of k labels, we construct by a very fast algorithm, a k-category proximal support vector machine (PSVM) classifier. Proximal support vectorâ€¦ (More)

- Glenn Fung
- 2001

A concave minimization approach is proposed for classifying unlabeled data based on the following ideas: (i) A small representative percentage (5% to 10%) of the unlabeled data is chosen by aâ€¦ (More)

- Glenn Fung, Olvi L. Mangasarian
- Neurocomputing
- 2003

An implicit Lagrangian [18] formulation of a support vector machine classifier that led to a highly effective iterative scheme [17] is solved here by a finite Newton method. The proposed method,â€¦ (More)

- Mark W. Schmidt, Kevin P. Murphy, Glenn Fung, RÃ³mer Rosales
- 2008 IEEE Conference on Computer Vision andâ€¦
- 2008

Coronary Heart Disease can be diagnosed by assessing the regional motion of the heart walls in ultrasound images of the left ventricle. Even for experts, ultrasound images are difficult to interpretâ€¦ (More)

- Glenn Fung, Sathyakama Sandilya, R. Bharat Rao
- KDD
- 2005

We describe an algorithm for converting linear support vector machines and any other arbitrary hyperplane-based linear classifiers into a set of non-overlapping rules that, unlike the originalâ€¦ (More)

- Glenn Fung, Olvi L. Mangasarian, Jude W. Shavlik
- NIPS
- 2002

Prior knowledge in the form of multiple polyhedral sets, each belonging to one of two categories, is introduced into a reformulation of a linear support vector machine classifier. The resultingâ€¦ (More)

- Yan Yan, RÃ³mer Rosales, Glenn Fung, Jennifer G. Dy
- ICML
- 2011

Obtaining labels can be expensive or timeconsuming, but unlabeled data is often abundant and easier to obtain. Most learning tasks can be made more efficient, in terms of labeling cost, byâ€¦ (More)