Browsing DMI Technical Reports by Title
Now showing items 423 of 46

Chunking for Massive Nonlinear Kernel Classification
(2006)A chunking procedure [2] utilized in [18] for linear classifiers is proposed here for nonlinear kernel classification of massive datasets. A highly accurate algorithm based on nonlinear support vector machines that ... 
CrossValidation, Support Vector Machines and Slice Models
(2001)We show how to implement the crossvalidation technique used in ma chine learning as a slice model. We describe the formulation in terms of support vector machines and extend the GAMS/DEA interface to allow for e cient ... 
Data Mining via Support Vector Machines
(2001)Support vector machines (SVMs) have played a key role in broad classes of problems arising in various elds. Much more recently, SVMs have become the tool of choice for problems arising in data classi  cation and mining. ... 
Data Selection for Support Vector Machine Classifiers
(2000)The problem of extracting a minimal number of data points from a large dataset, in order to generate a support vector machine (SVM) classi er, is formulated as a concave minimization problem and solved by a nite number ... 
Equivalence of Minimal L0 and Lp Norm Solutions of Linear Equalities, Inequalities and Linear Programs for Sufficiently Small p
(2011)For a bounded system of linear equalities and inequalities we show that the NPhard ?0 norm minimization problem min x0 subject to Ax = a, Bx ? b and x? ? 1, is completely equivalent to the concave minimization ... 
Exactness Conditions for a Convex Differentiable Exterior Penalty for Linear Programming
(2007)Sufficient conditions are given for a classical dual exterior penalty function of a linear program to be independent of its penalty parameter. This ensures that an exact solution to the primal linear program can be obtained ... 
FATCOP 2.0: Advanced Features in an Opportunistic Mixed Integer Programming Solver
(2000)We describe FATCOP 2.0, a new parallel mixed integer program solver that works in an opportunistic computing environment provided by the Condor resource management system. We outline changes to the search strategy of ... 
Feature Selection in kMedian Clustering
(2004)An e ective method for selecting features in clustering unlabeled data is proposed based on changing the objective function of the standard kmedian clustering algorithm. The change consists of perturbing the objective ... 
A Finite Newton Method for Classi cation Problems
(2001)A fundamental classi cation problem of data mining and machine learning is that of minimizing a strongly convex, piecewise quadratic function on the ndimensional real space Rn. We show nite termination of a Newton ... 
Finite Newton Method for Lagrangian Support Vector Machine Classi cation
(2002)An implicit Lagrangian [19] formulation of a support vector machine classi er that led to a highly e ective iterative scheme [18] is solved here by a nite Newton method. The proposed method, which is extremely fast and ... 
Incremental Support Vector Machine Classi cation
(2001)Using a recently introduced proximal support vector ma chine classi er [4], a very fast and simple incremental support vector machine (SVM) classi er is proposed which is capable of modifying an existing linear classi ... 
Interior Point Methods for Massive Support Vector Machines
(20000525)We investigate the use of interior point methods for solving quadratic programming problems with a small number of linear constraints where the quadratic term consists of a lowrank update to a positive semide nite matrix. ... 
KnowledgeBased Linear Programming
(2003)We introduce a class of linear programs with constraints in the form of implications. Such linear programs arise in support vector machine classi cation, where in addition to explicit datasets to be classi ed, prior knowledge ... 
KnowledgeBased Nonlinear Kernel Classi ers
(2003)Prior knowledge in the form of multiple polyhedral sets, each belonging to one of two categories, is introduced into a reformulation of a nonlinear kernel support vector machine (SVM) classi er. The resulting formulation ... 
KnowledgeBased Support Vector Machine Classi ers
(2001)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 classi er. The resulting formulation leads to a ... 
Lagrangian Support Vector Machines
(2000)An implicit Lagrangian for the dual of a simple reformulation of the standard quadratic program of a linear support vector machine is proposed. This leads to the minimization of an unconstrained di erentiable convex ... 
Large Scale Kernel Regression via Linear Programming
(1999)The problem of tolerant data tting by a nonlinear surface, in duced by a kernelbased support vector machine [24], is formulated as a linear program with fewer number of variables than that of other linear programming ... 
Massive Data Classification via Unconstrained Support Vector Machines
(2006)A highly accurate algorithm, based on support vector machines formulated as linear programs [13, 1], is proposed here as a completely unconstrained minimization problem [15]. Combined with a chunking procedure [2] this ... 
Multiple Instance Classification via Successive Linear Programming
(2005)The multiple instance classification problem [6,2,12] is formulated using a linear or nonlinear kernel as the minimization of a linear function in a finite dimensional (noninteger) real space subject to linear and bilinear ... 
A Newton Method for Linear Programming
(2002)A fast Newton method is proposed for solving linear programs with a very large ( 106) number of constraints and a moderate ( 102) number of variables. Such linear programs occur in data mining and machine learning. ...