K
Kristin P. Bennett
Researcher at Rensselaer Polytechnic Institute
Publications - 176
Citations - 10074
Kristin P. Bennett is an academic researcher from Rensselaer Polytechnic Institute. The author has contributed to research in topics: Support vector machine & Computer science. The author has an hindex of 45, co-authored 158 publications receiving 9250 citations. Previous affiliations of Kristin P. Bennett include University of Wisconsin-Madison & Catholic University of Leuven.
Papers
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Proceedings Article
Semi-Supervised Support Vector Machines
Kristin P. Bennett,Ayhan Demiriz +1 more
TL;DR: A general S3VM model is proposed that minimizes both the misclassification error and the function capacity based on all the available data that can be converted to a mixed-integer program and then solved exactly using integer programming.
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Robust linear programming discrimination of two linearly inseparable sets
TL;DR: A single linear programming formulation is proposed which generates a plane that of minimizes an average sum of misclassified points belonging to two disjoint points sets in n-dimensional real space, without the imposition of extraneous normalization constraints that inevitably fail to handle certain cases.
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Support vector machines: hype or hallelujah?
TL;DR: An intuitive explanation of SVMs from a geometric perspective is provided and the classification problem is used to investigate the basic concepts behind SVMs and to examine their strengths and weaknesses from a data mining perspective.
Journal Article
Dimensionality reduction via sparse support vector machines
TL;DR: The method constructs a series of sparse linear SVMs to generate linear models that can generalize well, and uses a subset of nonzero weighted variables found by the linear models to produce a final nonlinear model.
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Linear Programming Boosting via Column Generation
TL;DR: It is proved that for classification, minimizing the 1-norm soft margin error function directly optimizes a generalization error bound and is competitive in quality and computational cost to AdaBoost.