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Asa Ben-Hur

Researcher at Colorado State University

Publications -  104
Citations -  11957

Asa Ben-Hur is an academic researcher from Colorado State University. The author has contributed to research in topics: Protein function prediction & Support vector machine. The author has an hindex of 44, co-authored 98 publications receiving 10474 citations. Previous affiliations of Asa Ben-Hur include Stanford University & University of Washington.

Papers
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Journal ArticleDOI

Support vector clustering

TL;DR: In this paper, a Gaussian kernel based clustering method using support vector machines (SVM) is proposed to find the minimal enclosing sphere, which can separate into several components, each enclosing a separate cluster of points.
Journal ArticleDOI

A large-scale evaluation of computational protein function prediction

Predrag Radivojac, +107 more
- 01 Mar 2013 - 
TL;DR: Today's best protein function prediction algorithms substantially outperform widely used first-generation methods, with large gains on all types of targets, and there is considerable need for improvement of currently available tools.
Book ChapterDOI

A User's Guide to Support Vector Machines

TL;DR: This work provides a basic understanding of the theory behind SVMs and focuses on their use in practice, describing the effect of the SVM parameters on the resulting classifier, how to select good values for those parameters, data normalization, factors that affect training time, and software for training SVMs.
Journal ArticleDOI

Support vector machines and kernels for computational biology.

TL;DR: Support vector machines are widely used in computational biology due to their high accuracy, their ability to deal with high-dimensional and large datasets, and their flexibility in modeling diverse sources of data.
Proceedings Article

Result Analysis of the NIPS 2003 Feature Selection Challenge

TL;DR: The NIPS 2003 workshops included a feature selection competition organized by the authors, which took place over a period of 13 weeks and attracted 78 research groups and used a variety of methods for feature selection.