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Activity From Demographics and Linksshow
A Dynamic Adaptation of AD-trees for Efficient Machine Learning on Large Data Sets

This talk was presented at ICML 2000. It describes a modification to AD-trees to allow incremental and lazy growth. We discuss our implementation of these Dynamic AD-trees and present results for datasets with scores of high-arity attributes and millions of rows. ICML 2000.

These slides are best understood with the help of my notes from the presentation. These notes are available (linked) below.

Autonomous Fast Classifiers for Pharmaceutical Data Sets

This was an invited talk about my LR work for the Midwest Biopharmaceutical Statistics Workshop (MBSW) 2004. LR is relevant to some pharmaceutical problems, such as high throughput screening (HTS). This talk also highlights some of Ting Liu's fast k-nearest neighbor work. This talk was presented again at Applied Biosystems, Inc. in 2004.

Logistic Regression: Not Dead Yet

These slides were presented at Google, Inc, on 28 July 2005. The first half of these slides discusses why we like LR for data mining, and how we accelerate parameter fitting. The second half discusses several interesting and unusual applications of LR, most of which have software or papers available from the Auton lab website and the author's website.

Logistic Regression for Data Mining and High-Dimensional Classification

These are the slides from Paul Komarek's doctoral defense. The talk covers our exploration of logistic regression parameter fitting methods, our ultimate choice of methods, and some analysis and results. Not all of these slides were written to stand on their own.

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