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SV-Means: A Fast One-Class Support Vector Machine-Based Level Set Estimator

Pavy, Anne M.
http://orcid.org/0000-0002-1925-7231
http://rave.ohiolink.edu/etdc/view?acc_num=wright1516047120200949

Abstract Details

2017, Doctor of Philosophy (PhD), Wright State University, Electrical Engineering.
In this dissertation, a novel algorithm, SV-Means, is developed motivated by the many functions needed to perform radar waveform classification in an evolving, contested environment. Important functions include the ability to: reject classes not in the library, provide confidence in the classification decision, adapt the decision boundary on-the-fly, discover new classes, and quickly add new classes to the library. The SV-Means approach addresses these functions by providing a fast algorithm that can be used for anomaly detection, density estimation, open set classification, and clustering, within a Bayesian generative framework. The SV-Means algorithm extends the quantile one-class support vector machine (q-OCSVM) density estimation algorithm into a classification formulation with inspiration from k-means and stochastic gradient descent principles. In addition, the algorithm can be trained at least an order of magnitude faster than the q-OCSVM and other OCSVM algorithms. SV-Means has been thoroughly tested with a phase-modulated radar waveform data set, and several data sets from the University of California Irvine (UCI) machine learning repository, in each application area except clustering. In clustering, a novel algorithm, SV-Means Level Set Clustering, was formulated using the SV-Means algorithm as a first step to determine the number of clusters per level set and distinguish overlapping clusters. Finally, an end-to-end demonstration from training, to testing, to clustering, to adding a new class to the library, was demonstrated using the SV-Means algorithm.
Brian Rigling, Ph.D. (Advisor)
Fred Garber, Ph.D. (Committee Member)
Kefu Xue, Ph.D. (Committee Member)
Michael Bryant, Ph.D. (Committee Member)
Randolph Moses, Ph.D. (Committee Member)
103 p.
Electrical Engineering
open set classification; one-class support vector machine

Recommended Citations

Citations

  • Pavy, A. M. (2017). SV-Means: A Fast One-Class Support Vector Machine-Based Level Set Estimator [Doctoral dissertation, Wright State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=wright1516047120200949

    APA Style (7th edition)

  • Pavy, Anne. SV-Means: A Fast One-Class Support Vector Machine-Based Level Set Estimator. 2017. Wright State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=wright1516047120200949.

    MLA Style (8th edition)

  • Pavy, Anne. "SV-Means: A Fast One-Class Support Vector Machine-Based Level Set Estimator." Doctoral dissertation, Wright State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=wright1516047120200949

    Chicago Manual of Style (17th edition)

wright1516047120200949
818
© 2017, all rights reserved.
This open access ETD is published by Wright State University and OhioLINK.