Cart (Loading....) | Create Account
Close category search window
 

Efficient Additive Models via the Generalized Lasso

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Semenovich, D. ; Sch. of Comput. Sci. & Eng., Univ. of New South Wales, Sydney, NSW, Australia ; Morioka, N. ; Sowmya, A.

We propose a framework for learning generalized additive models at very little additional cost (a small constant) compared to some of the most efficient schemes for learning linear classifiers such as linear SVMs and regularized logistic regression. We achieve this through a simple feature encoding scheme followed by a novel approach to regularization which we term ``generalized lasso''. Addtive models offer an attractive alternative to linear models for many large scale tasks as they have significantly higher predictive power while remaining easily interpretable. Furthermore, our regularizations approach extends to arbitrary graphs, allowing, for example, to explicitly incorporate spatial information or similar priors. Traditional approaches for learning additive models, such as back fitting, do not scale to large datasets. Our new formulation of the resulting optimization problem allows us to investigate the use of recent accelerated gradient algorithms and demonstrate speed comparable to state of the art linear SVM training methods, making additive models suitable for very large problems. In our experiments we find that additive models consistently outperform linear models on various datasets.

Published in:

Data Mining Workshops (ICDMW), 2010 IEEE International Conference on

Date of Conference:

13-13 Dec. 2010

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.