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- Ayhan Demiriz, Kristin P. Bennett, John Shawe-Taylor
- Machine Learning
- 2002

We examine linear program (LP) approaches to boosting and demonstrate their efficient solution using LPBoost, a column generation based simplex method. We formulate the problem as if all possibleâ€¦ (More)

- Kristin P. Bennett, Ayhan Demiriz
- NIPS
- 1998

We introduce a semi-supervised support vector machine (SVM) method. Given a training set of labeled data and a working set of unlabeled data, SVM constructs a support vector machine using both theâ€¦ (More)

A single linear programming formulation is proposed which generates a plane that minimizes an average sum of misclassi ed points belonging to two disjoint points sets in n-dimensional real space.â€¦ (More)

- Kristin P. Bennett, Erin J. Bredensteiner
- ICML
- 2000

We develop an intuitive geometric interpretation of the standard support vector machine (SVM) for classification of both linearly separable and inseparable data and provide a rigorous derivation ofâ€¦ (More)

- Kristin P. Bennett, Colin Campbell
- SIGKDD Explorations
- 2000

Support Vector Machines (SVMs) and related kernel methods have become increasingly popular tools for data mining tasks such as classification, regression, and novelty detection. The goal of thisâ€¦ (More)

- Erin J. Bredensteiner, Kristin P. Bennett
- Comp. Opt. and Appl.
- 1999

We examine the problem of how to discriminate between objects of three or more classes. Specifically, we investigate how two-class discrimination methods can be extended to the multiclass case. Weâ€¦ (More)

- Jinbo Bi, Kristin P. Bennett, Mark J. Embrechts, Curt M. Breneman, Minghu Song
- Journal of Machine Learning Research
- 2003

We describe a methodology for performing variable ranking a d selection using support vector machines (SVMs). The method constructs a series of sparse li near SVMs to generate linear models that canâ€¦ (More)

An adaptive semi-supervised ensemble method, ASSEMBLE, is proposed that constructs classification ensembles based on both labeled and unlabeled data. ASSEMBLE alternates between assigningâ€¦ (More)

Key ideas from statistical learning theory and support vector machines are generalized to decision trees. A support vector machine is used for each decision in the tree. The \optimal" decision treeâ€¦ (More)

- Colin Campbell, Kristin P. Bennett
- NIPS
- 2000

Novelty detection involves modeling the normal behaviour of a system hence enabling detection of any divergence from normality. It has potential applications in many areas such as detection ofâ€¦ (More)