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Anomaly Detection Through a Bayesian SVM

Anomaly Detection Through a Bayesian SVM

Anomaly Detection Through a Bayesian SVM. Vasilis A. Sotiris AMSC 664 Final Presentation May 6 th 2008 Advisor: Dr. Michael Pecht University of Maryland College Park, MD 20783. Objectives. Develop an algorithm to detect anomalies in electronic systems (large multivariate datasets)

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335 views • 32 slides
Pattern Recognition and Machine Learning

Pattern Recognition and Machine Learning

Pattern Recognition and Machine Learning. Chapter 7: sparse kernel Machines. Outline. The problem: finding a sparse decision (and regression) machine that uses kernels The solution: Support Vector Machines (SVMs) and Relevance Vector Machines (RVMs) The core ideas behind the solutions

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691 views • 26 slides
Classification of Ovarian Tumors Using Bayesian Least Squares Support Vector Machines

Classification of Ovarian Tumors Using Bayesian Least Squares Support Vector Machines

Classification of Ovarian Tumors Using Bayesian Least Squares Support Vector Machines. C. Lu 1 , T. Van Gestel 1 , J. A. K. Suykens 1 , S. Van Huffel 1 , D. Timmerman 2 , I. Vergote 2 1 Department of Electrical Engineering, Katholieke Universiteit Leuven, Leuven, Belgium,

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419 views • 20 slides
Classification of Ovarian Tumors Using Bayesian Least Squares Support Vector Machines

Classification of Ovarian Tumors Using Bayesian Least Squares Support Vector Machines

Classification of Ovarian Tumors Using Bayesian Least Squares Support Vector Machines. C. Lu 1 , T. Van Gestel 1 , J. A. K. Suykens 1 , S. Van Huffel 1 , D. Timmerman 2 , I. Vergote 2 1 Department of Electrical Engineering, Katholieke Universiteit Leuven, Leuven, Belgium,

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236 views • 20 slides

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