Machine Learning Using Support Vector Machines

Jul 26, 2012

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Paper Information. Title: Machine Learning Using Support Vector Machines.Authors:Abdul Rahim Ahmad.Marzuki Khalid.Rubiyah Yusof.Publisher: MSTC 2002, Johor Bahru.Date: September 2002.. Review Outlines. IntroductionArtificial Neural Network (ANN)Support Vector Machine (SVM)Support Vector

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Machine Learning Using Support Vector Machines

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1. Machine Learning Using Support Vector Machines (Paper Review) Presented to: Prof. Dr.�Mohamed Batouche Prepared By: Asma B. Al-Saleh Amani A. Al-Ajlan 427220094 427220111

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Support Vector Machines

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CS 9633 Machine Learning Support Vector Machines

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Support Vector Machines . More information can be found in http://www.cs.cmu.edu/~awm/tutorials. a. Linear Classifiers. x. f . y est. f ( x , w ,b ) = sign( w . x - b ). denotes +1 denotes -1. How would you classify this data?. a. Linear Classifiers. x. f . y est.

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Support Vector Machines

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Support Vector Machines. Presented By Sherwin Shaidaee. Papers. Vladimir N. Vapnik, “The Statistical Learning Theory”. Springer, 1998 Yunqiang Chen, Xiang Zhou, and Thomas S. Huang, University of Illinois, “ONE-CLASS SVM FOR LEARNING IN IMAGE RETRIEVAL”, 2001

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Support Vector Machines. Text Book Slides. Find a linear hyperplane (decision boundary) that will separate the data. Support Vector Machines. One Possible Solution. Support Vector Machines. Another possible solution. Support Vector Machines. Other possible solutions.

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Summer Course: Data Mining. Support Vector Machines. Support Vector Machines and other penalization classifiers. Presenter: Georgi Nalbantov. Presenter: Georgi Nalbantov. August 2009. Contents. Purpose Linear Support Vector Machines Nonlinear Support Vector Machines

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Support Vector Machines. Instructor Max Welling ICS273A UCIrvine. Philosophy. First formulate a classification problem as finding a separating hyper-plane that maximizes “the margin”. Allow for errors in classification using “slack-variables”. Convert problem to the “dual problem”.

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Support Vector Machines

Support Vector Machines. 1. Introduction to SVMs 2. Linear SVMs 3. Non-linear SVMs. References: 1. S.Y. Kung, M.W. Mak, and S.H. Lin. Biometric Authentication: A Machine Learning Approach, Prentice Hall, to appear.

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Support Vector Machines

Support Vector Machines. Pattern Recognition Sergios Theodoridis Konstantinos Koutroumbas Second Edition A Tutorial on Support Vector Machines for Pattern Recognition Data Mining and Knowledge Discovery, 1998 C. J. C. Burges. Separable Case. Maximum Margin Formulation. Separable Case.

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Machine Learning Neural Networks, Support Vector Machines

Machine Learning Neural Networks, Support Vector Machines. Georg Dorffner Section for Artificial Intelligence and Decision Support CeMSIIS – Medical University of Vienna. Machine Learning – possible definitions.

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