Don't miss this Great Talk on Ensemble Methods
Tricia Hoffman
Dear Students,
I invited Giovanni to speak at the ACM on Monday night. He is the one who wrote the book that I recommended last quarter. It should be a great talk.
Warm Regards, TriciaDMSIG – On Diversity, Complexity, and Regularization in Ensemble Models, January 24, 2011
LOCATION: LinkedIn, 2025 Stierlin Ct, Mountain View, CA 94043
Date: Monday January 24, 2011; 6:30 pm 6:30 – 9:00 pm (6:30 – 7:00 networking & snacks; 7:00 – 7:10 announcements; 7:10+ presentation, Q&A)
Cost: Free and open to all who wish to attend, but membership is only $20/year. Anyone may join our mailing list at no charge, and receive announcements of upcoming events.
Speakers: Giovanni Seni, PhD
Title: On Diversity, Complexity, and Regularization in Ensemble Models
Abstract:
The discovery of ensemble methods is one of the most influential developments in Data Mining and Machine Learning in the past decade. These methods combine multiple models into a single predictive system that is more accurate than even the best of its components. The use of ensemble methods can provide a critical boost to existing systems addressing the hardest of industrial challenges – from investment timing to drug discovery, from fraud detection to recommendation systems – where predictive accuracy is vital. This talk, based on a recently published book by the speaker, offers a concise introduction to this breakthrough topic. After a sketch of the major concerns in predictive learning, the talk will give an overview of regularization, a key concept driving the superior performance of modern ensemble algorithms. It then takes a shortcut into the heart of the popular tree-based ensemble creation strategies using recent developments from the frontiers of statistics, where research efforts are now focused to explain and harness the mysteries of ensembles.
Biography:
Giovanni Seni is a Senior Scientist with Elder Research, Inc. (ERI) and directs ERI’s Western office. As an active data mining practitioner in Silicon Valley, he has over 15 years R&D experience in statistical pattern recognition, data mining, and human-computer interaction applications. He has been a member of the technical staff at large technology companies, and a contributor at smaller organizations. He holds five US patents and has published over twenty conference and journal articles. His book with John Elder, “Ensemble Methods in Data Mining – Improving accuracy through combining predictions”, was published in February 2010 by Morgan & Claypool. Giovanni is also an adjunct faculty at the Computer Engineering Department of Santa Clara University, where he teaches an Introduction to Pattern Recognition and Data Mining class.
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Patricia Hoffman PhD
Chair Association of Computing Machinery - Data Mining SIG
hoffman...@gmail.com
http://www.patriciahoffmanphd.com/
http://www.linkedin.com/in/patriciahoffmanphd
http://twitter.com/patriciahoffman
Past Data Mining Classes
http://machinelearning2010fall.pbworks.com/w/page/30032895/FrontPage
http://machinelearning123.pbworks.com/w/page/26270704/FrontPage
Future Machine Learning Classes
Machine Learning 101: Learn about ML algorithms and implement them in r
Machine Learning 102: Enable you to read and implement algorithms from current papers
Machine Learning 201: Advanced Regression Techniques, Generalized Linear Models, and Generalized Additive Models
Machine Learning 202: Collaborative Filtering, Bayesian Belief Networks, and Advanced Trees