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Introduction to Support Vector Machines for Data Mining. Mahdi Nasereddin Ph.D. Pennsylvania State University School of Information Sciences and Technology. Agenda. Introduction Support Vector Machines Preliminary Experimentation Conclusion Questions?. Data Mining Techniques:.
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Introduction to Support Vector Machines for Data Mining Mahdi Nasereddin Ph.D. Pennsylvania State University School of Information Sciences and Technology
Agenda • Introduction • Support Vector Machines • Preliminary Experimentation • Conclusion • Questions?
Data Mining Techniques: • Neural Networks • Decision Trees • Multivariate Adaptive Regression Splines (MARS) • Rule Induction • Nearest Neighbor Method and discriminant analysis • Genetic Algorithms • Support Vector Machines
Support Vector Machines • First introduced by Vapnik and Chervonenkis in COLT-92 • Bases on Statistical Learning Theory • Applications • Basic Theory • Classification • Regression
Successful Applications of SVMS • Protein Structure Prediction http://www.cs.umn.edu/~hpark/papers/surface.pdf • Intrusion Detection www.cs.nmt.edu/~IT • Handwriting Recognition • Detecting Steganography in digital images http://www.cs.dartmouth.edu/~farid/publications/ih02.html
Successful Applications of SVMS • Breast Cancer Prognosis: Chemotherapy Effect on Survival Rate (Lee, Mangasarian and Wolberg, 2001) • Particle and Quark-Flavour Identification in High Energy Physics (http://wwwrunge.physik.uni-freiburg.de/preprints/EHEP9901.ps) • Function Approximation
SVM for Regression • In case of regression, the goal is to construct a hyperplane that is close to as many points as possible. • For both classification and regression, learning is done via quadratic programming (one optimum point)
Strengths and Weaknesses of SVM • Strengths • Training is relatively easy • No local optimal, unlike in neural networks • It scales relatively well to high dimensional data • Weaknesses • Need a “good” kernel function
Preliminary Experimentation: Forecasting GDP using Oil Prices (with F. Malik) • Forecasting model • Objective: To predict the Gross Domestic Product (GDP) for the next quarter using • Oil prices (including time lag) • GDP time
Data Set • We looked at quarterly Oil prices and GDP data • January 1947 – December 2002 • Oil price data were obtained from Bureau of Labor Statistics • GDP data were obtained from the Bureau of Economic Analysis. • We used the growth rate of GDP and the growth rate of oil prices.
Models • Neural Networks • Back-propagation • One hidden layer • Delta rule was used for training • LS-SVM (Van Gestel, 2001) • Matlab toolbox
Experimentation • Created the training data to predict the last 40 quarters GDP (test data) • Trained the neural network and the SVM • Used the model to predict GDP, and calculated the error of prediction
Good References • Introductions • Martin Law, “An Introduction to Support Vector Machines” • Andrew More, “Support Vector Machines” www.cs.cmu.edu/~awm • N. Cristianini www.support-vector.net/tutorial.html • In depth • Support Vector Machines book www.support-vector.net
Questions • E-mail: mxn16@psu.edu • Presentation will be posted (by Friday) at http://www.bklv.psu.edu/faculty/nasereddin