Support vector machines (SVMs) have established themselves as one of the preeminent machine learning models for classification and regression over the past decade or so, frequently outperforming artificial neural networks in task such as text mining and bioinformatics. Dr. Lutz Hamel, author of "Knowledge Discovery with Support Vector Machines" from Wiley will present his online course Introduction to Support Vector Machines In R Nov. 20 Dec. 18 at statistics.com.
Support Vector Machines in R will give you an understanding on what is going on "under the hood" when using SVMs. After completing this course, you will be able to interpret the performance of SVM models and make appropriate choices for model parameters during the model evaluation and selection cycle. You will understand the difference between linear, polynomial, and gaussian kernels and know how to tune their parameters. In addition, you will have a deep understanding on how the cost constant "C" affects the quality of your models. Dr. Lutz Hamel teaches at the University of Rhode Island and was the founder of the machine learning and data mining group there. He is the author of Knowledge Discovery with Support Vector Machines (the course text). Before becoming an academic, Dr. Hamel was Director of Software Development at Thinking Machine Corporation, and Vice President of R&D for Bluestreak, where he oversaw the development of advanced technologies for online ad delivery and optimization, and directed the building of a next generation data warehouse-driven system for campaign analysis and design tools. Participants can ask questions and exchange comments with Dr. Hamel via a private discussion board throughout the course. Details and registration: http://www.statistics.com/ourcourses/SVM/ Thanks for your consideration, Janet Dobbins -- statistics.com the source for statistics education 612 N. Jackson St. Arlington, VA 22201 703.522.5410 703.522.5846-fax jdobb...@statistics.com [[alternative HTML version deleted]]
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