Kristin P. Bennett

ABSTRACT Exploiting Unlabeled Data in Ensemble Methods (2008)

We develop an intuitive geometric framework for support vector regression (SVR). By examining when ɛ-tubes exist, we show that SVR can be regarded as a classification problem in the dual space. Hard...

Donghui Wu, Kristin P. Bennett, Nello Cristianini, John Shawe-taylor, Royal Holloway

An adaptive semi-supervised ensemble method, ASSEM-BLE, is proposed that constructs classification ensembles based on both labeled and unlabeled data. ASSEMBLE alternates between assigning...

bristol.ac.uk (2007)

The problem of controlling the capacity of decision trees is considered for the case where the decision nodes implement linear threshold functions. In addition to the standard early stopping and...

Chapter 1 OPTIMIZATION APPROACHES TO SEMI-SUPER- VISED LEARNING (2007)

ABSTRACT Exploiting Unlabeled Data in Ensemble Methods (2007)

Abstract We examine mathematical models for semi-supervised support vector machines (S VM). Given a training set of labeled data and a working set of unlabeled data, S VM constructs a support vector...

Hybrid Extreme Point Tabu Search (2007)

An adaptive semi-supervised ensemble method, ASSEM-BLE, is proposed that constructs classification ensembles based on both labeled and unlabeled data. ASSEMBLE alternates between assigning...

Jennifer A. Blue, Kristin P. Bennett

We develop a new hybrid tabu search method for optimizing a continuous differentiable function over the extreme points of a polyhedron. The method combines extreme point tabu search with traditional...

Support Vector Machine Regression in (2007)

Ayhan Demiriz, Kristin P. Bennett, Curt M. Breneman, Mark J. Embrechts

Predicting the biological activity of a compound from its chemical structure is a fundamental problem in drug design. The ability exists to generate vast amounts of potential pharmecutical compounds....

ABSTRACT Exploiting Unlabeled Data in Ensemble Methods (2007)

To appear in Optimization Methods and Software ROBUST LINEAR PROGRAMMING DISCRIMINATION OF TWO LINEARLY INSEPARABLE SETS (2007)

An adaptive semi-supervised ensemble method, ASSEM-BLE, is proposed that constructs classification ensembles based on both labeled and unlabeled data. ASSEMBLE alternates between assigning...

c ○ 2000 Kluwer Academic Publishers. Manufactured in The Netherlands. Enlarging the Margins in Perceptron Decision Trees (2007)

A single linear programming formulation is proposed which generates a plane that minimizes an average sum of misclassified points belonging to two disjoint points sets in n-dimensional real space....

Kristin P. Bennett, Robert Schapire

Abstract. Capacity control in perceptron decision trees is typically performed by controlling their size. We prove that other quantities can be as relevant to reduce their flexibility and combat...

Proteomics reveals multiple routes to the osteogenic phenotype in mesenchymal stem cells (2007)

Bennett, Kristin P, Bergeron, Charles, Acar, Evrim, Klees, Robert F, Vandenberg, Scott L, Yener, Bülent, ...

Abstract Background Recently, we demonstrated that human mesenchymal stem cells (hMSC) stimulated with dexamethazone undergo gene focusing during osteogenic differentiation ( Stem Cells Dev 14(6):...

Bilevel model selection for support vector machines (2007)

Gautam Kunapuli, Kristin P. Bennett, Jing Hu, Jong-shi Pang

Abstract. The successful application of Support Vector Machines (SVMs), kernel methods and other statistical machine learning methods requires selection of model parameters based on estimates of the...

BioMed Central (2007)

Bmc Genomics, Kristin P Bennett, Charles Bergeron, Evrim Acar, Robert F Klees, Scott L V, ...

Research article Proteomics reveals multiple routes to the osteogenic phenotype in mesenchymal stem cells

On the Global Solution of Linear Programs with Linear Complementarity Constraints ∗† (2007)

Jing Hu, John E. Mitchell, Jong-shi Pang, Kristin P. Bennett, Gautam Kunapuli

This paper presents a parameter-free integer-programming based algorithm for the global resolution of a linear program with linear complementarity constraints (LPEC). The cornerstone of the algorithm...

The Interplay of Optimization and Machine Learning Research (2006)

Kristin P. Bennett, P. Bennett, Emilio Parrado-Hernandez

The fields of machine learning and mathematical programming are increasingly intertwined. Optimization problems lie at the heart of most machine learning approaches. The Special Topic on Machine...

Model selection via bilevel optimization (2006)

Kristin P. Bennett, Jing Hu, Gautam Kunapuli, Jong-shi Pang

Abstract — A key step in many statistical learning methods used in machine learning involves solving a convex optimization problem containing one or more hyper-parameters that must be selected by...

Large scale multiple kernel learning (2006)

Sören Sonnenburg, Bernhard Schölkopf Bernhard, Emilio Parrado-hernández, Kristin P. Bennett

While classical kernel-based learning algorithms are based on a single kernel, in practice it is often desirable to use multiple kernels. Lankriet et al. (2004) considered conic combinations of...

Dimensionality Reduction via Sparse Support Vector Machines (2003)

Jinbo Bi, Kristin P. Bennett, Mark Embrechts, Curt M. Breneman, Minghu Song, Isabelle Guyon, ...

We describe a methodology for performing variable ranking and selection using support vector machines (SVMs). The method constructs a series of sparse linear SVMs to generate linear models that can...

Dimensionality Reduction via Sparse Support Vector Machines (2003)

Jinbo Bi, Kristin P. Bennett, Mark Embrechts, Curt M. Breneman, Minghu Song, Isabelle Guyon, ...

We describe a methodology for performing variable ranking and selection using support vector machines (SVMs). The method constructs a series of sparse linear SVMs to generate linear models that can...

Dimensionality Reduction via Sparse Support Vector Machines (2003)

Jinbo Bi, Kristin P. Bennett, Mark Embrechts, Curt M. Breneman, Minghu Song, Isabelle Guyon, ...

We describe a methodology for performing variable ranking and selection using support vector machines (SVMs). The method constructs a series of sparse linear SVMs to generate linear models that can...

Dimensionality Reduction via Sparse Support Vector Machines (2003)

Jinbo Bi, Kristin P. Bennett, Mark Embrechts, Curt M. Breneman, Minghu Song, Isabelle Guyon, ...

We describe a methodology for performing variable ranking and selection using support vector machines (SVMs). The method constructs a series of sparse linear SVMs to generate linear models that can...

Regression Error Characteristic CurVes (2003)

A pattern search method for model selection of support vector regression (2002)

Receiver Operating Characteristic (ROC) curves provide a powerful tool for visualizing and comparing classification results. Regression Error Characteristic (REC) curves generalize ROC curves to...

Michinari Momma, Kristin P. Bennett

We develop a fully-automated pattern search methodology for model selection of support vector machines (SVMs) for regression and classification. Pattern search (PS) is a derivative-free optimization...

A genetic algorithm approach for semi-supervised clustering (2002)

Ayhan Demiriz, Kristin P. Bennett, Mark J. Embrechts

A novel semi-supervised clustering algorithm is proposed that synergizes the benefits of supervised and unsupervised learning methods. Data are clustered using an unsupervised learning technique...

Linear programming boosting via column generation (2002)

Mark: a boosting algorithm for heterogeneous kernel models (2002)

1 Introduction Recent papers [20] have shown that boosting, arcing, and related ensemble methods (hereafter summarized asboosting) can be viewed as margin maximization in function space. By changing...

Duality, Geometry, and Support Vector Regression (2002)

Exploiting unlabeled data in ensemble methods (2002)

We develop an intuitive geometric framework for support vector regression (SVR). By examining when ɛ-tubes exist, we show that SVR can be regarded as a classification problem in the dual space. Hard...

Kristin P. Bennett, Ayhan Demiriz, Richard Maclin

An adaptive semi-supervised ensemble method, ASSEMBLE, is proposed that constructs classification ensembles based on both labeled and unlabeled data. ASSEMBLE alternates between assigning...

Duality, Geometry, and Support Vector Regression (2002)

Linear programming boosting via column generation (2002)

We develop an intuitive geometric framework for support vector regression (SVR). By examining when #-tubes exist, we show that SVR can be regarded as a classification problem in the dual space. Hard...

Duality, Geometry, and Support Vector Regression (2002)

We examine linear program (LP) approaches to boosting and demonstrate their e#cient solution using LPBoost, a column generation based simplex method. We formulate the problem as if all possible weak...

Linear programming boosting via column generation (2002)

We develop an intuitive geometric framework for support vector regression (SVR). By examining when #-tubes exist, we show that SVR can be regarded a classification problem in the dual space. Hard and...

Linear programming boosting via column generation (2002)

We examine linear program (LP) approaches to boosting and demonstrate their ecient solution using LPBoost, a column generation based simplex method. We formulate the problem as if all possible weak...

Linear programming boosting via column generation (2002)

We examine linear program (LP) approaches to boosting and demonstrate their e#cient solution using LPBoost, a column generation based simplex method. We formulate the problem as if all possible weak...

A Pattern Search Method for Model Selection of (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 possible...

Support Vector Regression, Michinari Momma, Kristin P. Bennett

We develop a fully-automatic pattern search methodology for model selection of support vector machines (SVMs) for regression and classification.

Duality, Geometry, and Support Vector Regression (2002)

Support vector machine regression in chemometrics (2001)

We develop an intuitive geometric framework for support vector regression (SVR). By examining when ǫ-tubes exist, we show that SVR can be regarded a classification problem in the dual space. Hard...

Ayhan Demiriz, Kristin P. Bennett, Curt M. Breneman, Mark J. Embrechts

Predicting the biological activity of a compound from its chemical structure is a fundamental problem in drug design. The ability exists to generate vast amounts of potential pharmecutical compounds....

A Linear Programming Approach to Novelty Detection (2001)

Colin Campbell, Kristin P. Bennett

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 of...

A Column Generation Algorithm for Boosting (2000)

Bennett, Kristin P., Demiriz, Ayhan, Shawe-Taylor, John

A Column Generation Algorithm for Boosting (2000)

Bennett, Kristin P., Demiriz, Ayhan, Shawe-Taylor, John

A Column Generation Algorithm for Boosting (2000)

Bennett, Kristin P., Demiriz, Ayhan, Shawe-Taylor, John

Support Vector Machines: Hype or Hallelujah (2000)

Duality and Geometry in SVM Classifiers (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 this...

Optimization Approaches to Semi-Supervised Learning (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 of...

Duality and geometry in SVM classifiers (2000)

We examine mathematical models for semi-supervised support vector machines (S 3 VM). Given a training set of labeled data and a working set of unlabeled data, S 3 VM constructs a support vector...

Large Margin Decision Trees for Induction and Transduction (1999)

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 of...

Wu, Donghui, Bennett, Kristin P., Cristianini, Nello, Shawe-Taylor, John

Large Margin Decision Trees for Induction and Transduction (1999)

Wu, Donghui, Bennett, Kristin P., Cristianini, Nello, Shawe-Taylor, John

Large Margin Decision Trees for Induction and Transduction (1999)

Wu, Donghui, Bennett, Kristin P., Cristianini, Nello, Shawe-Taylor, John

Enlarging the Margins in Perceptron Decision Trees (1999)

Donghui Wu, Kristin P. Bennett, Nello Cristianini, John Shawe-taylor

1 Abstract Capacity control in perceptron decision trees is typically performed by controlling their size. We prove that other quantities can be as relevant to reduce their flexibility and combat...

Semi-supervised clustering using genetic algorithms (1999)

Ayhan Demiriz, Kristin P. Bennett, Mark J. Embrechts

A semi-supervised clustering algorithm is proposed that combines the benefits of supervised and unsupervised learning methods. Data are segmented /clustered using an unsupervised learning technique...

Multicategory classification by support vector machines (1999)

Enlarging the Margins in Perceptron Decision Trees (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. We...

Kristin Bennett Department, Kristin P. Bennett, Nello Cristianini, John Shawe-taylor, Donghui Wu

Capacity control in perceptron decision trees is typically performed by controlling their size. We prove that other quantities can be as relevant to reduce their flexibility and combat overfitting....

Enlarging the Margins in Perceptron Decision Trees (1999)

Kristin Bennett Department, Kristin P. Bennett, Nello Cristianini, John Shawe-taylor, Donghui Wu

Capacity control in perceptron decision trees is typically performed by controlling their size. We prove that other quantities can be as relevant to reduce their flexibility and combat overfitting....

Density-Based Indexing for Approximate Nearest-Neighbor Queries (1999)

Kristin P. Bennett, Usama Fayyad, Dan Geiger

We consider the problem of performing nearest-neighbor queries efficiently over large high-dimensional databases. Assuming that a full database scan to determine the nearest neighbor entries is not...

Large Margin Trees for Induction and Transduction (1999)

Donghui Wu, Kristin P. Bennett, Nello Cristianini, John Shawe-taylor

The problem of controlling the capacity of decision trees is considered for the case where the decision nodes implement linear threshold functions. In addition to the standard early stopping and...

Enlarging the Margins in Perceptron Decision Trees (1999)

Kristin P. Bennett, Nello Cristianini, John Shawe-taylor, Donghui Wu

Capacity control in perceptron decision trees is typically performed by controlling their size. We prove that other quantities can be as relevant to reduce their flexibility and combat overfitting....

Semi-supervised support vector machines (1998)

Kristin P. Bennett, Ayhan Demiriz

We introduce a semi-supervised support vector machine (S 3 VM) method. Given a training set of labeled data and a working set of unlabeled data, S 3 VM constructs a support vector machine using both...

Semi-supervised support vector machines (1998)

Kristin P. Bennett, Ayhan Demiriz

We introduce a semi-supervised support vector machine (S

Feature minimization within decision trees (1998)

Feature minimization within decision trees (1998)

Decision trees for classification can be constructed using mathematical programming. Within decision tree algorithms, the feature minimization problem is to construct accurate decisions using as few...

Feature minimization within decision trees (1998)

Decision trees for classification can be constructed using mathematical programming. Within decision tree algorithms, the feature minimization problem is to construct accurate decisions using as few...

On support vector decision trees for database marketing (1998)

Kristin P. Bennett, Leonardo Auslender, Donghui Wu, Sagamore Ave

We introduce a support vector decision tree method for customer targeting in the framework of large databases (database marketing). The goal is to provide a tool to identify the best customers based...

Geometry in Learning (1997)

An Extreme Point Tabu Search Method for Data Mining (1996)

One of the fundamental problems in learning is identifying members of two different classes. For example, to diagnose cancer, one must learn to discriminate between benign and malignant tumors....

Kristin P. Bennett, Jennifer A. Blue

We propose an Extreme Point Tabu Search (EPTS) algorithm that constructs globally optimal decision trees for classification problems. Typically, decision tree algorithms are greedy. They optimize the...

Optimal Decision Trees (1996)

Kristin P. Bennett, Jennifer A. Blue

We propose an Extreme Point Tabu Search (EPTS) algorithm that constructs globally optimal decision trees for classification problems. Typically, decision tree algorithms are greedy. They optimize the...

Hybrid Extreme Point Tabu Search (1996)

Jennifer A. Blue, Kristin P. Bennett

We develop a new hybrid tabu search method for optimizing a continuous differentiable function over the extreme points of a polyhedron. The method combines extreme point tabu search with traditional...

Drug Recalls: (1995)

Kristin P. Bennett, E. G. Seldane, Bextra Pain Relief, Paladone Pain Relief, Baycol Cholesterol, Phenyl-propanolamine Decongestant, ...

• 10-15 years from conception � market for drug • First-year sales> $1B/drug

A Parametric Optimization Method for Machine Learning (1995)

Serial and Parallel Multicategory Discrimination (1994)

The classification problem of constructing a plane to separate the members of two sets can be formulated as a parametric bilinear program. This approach was originally created to minimize the number...

Machine learning via mathematical programming /--by Kristin P. Bennett. (1993)

A parallel algorithm is proposed for a fundamental problem of machine learning, that of multicategory discrimination. The algorithm is based on minimizing an error function associated with a set of...

Bennett, Kristin P.

Cover title.

Machine learning via mathematical programming /--by Kristin P. Bennett. (1993)

Bennett, Kristin P.

Typescript.

Bilinear Separation of Two Sets in n-Space (1993)

Kristin Bennett Mangasarian, Kristin P. Bennett, O. L. Mangasarian

The NP-complete problem of determining whether two disjoint point sets in the n-dimensional real space R n can be separated by two planes is cast as a bilinear program, that is minimizing the scalar...

Bilinear Separation of Two Sets in n-Space (1993)

Multicategory Discrimination via Linear Programming (1992)

The NP-complete problem of determining whether two disjoint point sets in the n-dimensional real space R n can be separated by two planes is cast as a bilinear program, that is minimizing the scalar...