Support vector machines (SVMs) began as more sophisticated
cousins of Fisher's discriminant classifier, growing in
flexibility and complexity as computational methods have
facilitated extensions (including to nonlinear separation of
classes). Established as one of the preeminent machine learning
models for classification and regression over the past decade or so,
SVMs frequently outperform artificial neural networks in tasks such as
text mining and bioinformatics. Dr. Lutz Hamel, author of “Knowledge
Discovery with Support Vector Machines”, (the course text, from Wiley)
presents his online course “Introduction to Support Vector Machines
In R” Nov. 19 – Dec. 17.
Nov 12: Spatial Statistics with Geographic Information Systems
Nov 19: Bayesian Regression Modeling via MCMC Techniques
Nov 19: Introduction to Support Vector Machines in R (more below)
Nov 26: Data Mining: SAS Enterprise Miner Practicum
The aim of “Support Vector Machines in R” is to 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.
The course is based on the R statistical computing environment.
However, the knowledge gained here is easily transferred to other
knowledge discovery environments.
Dr. Lutz Hamel teaches at the University of Rhode Island and founded
the machine learning and data mining group there. Prior to his
academic post, 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.
The course takes place online at statistics.com in a series of 4
Weekly lessons and assignments, and requires about 15 hours/week.
Participate at your own convenience; there are no set times when
you are required to be online.
You may leave the list at any time by sending the command
to [log in to unmask], leaving the subject line blank.