DMSIG – On Diversity, Complexity, and Regularization in Ensemble Mo...

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)

http://www.sfbayacm.org/

Cost: Free and open to all who wish to attend, but membership is only $20/year. Anyone may join our mailing list at no

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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.