A General Framework for Accurate and Fast Regression by Data Summarization in Random Decision Trees
A General Framework for Fast and Accurate Regression by Data Summarization in Random Decision Trees Wei Fan, IBM T.J.Watson Joe McCloskey, US Department.
Systematic Data Selection to Mine Concept Drifting Data Streams Wei Fan IBM T.J.Watson.
On the Optimality of Probability Estimation by Random Decision Trees Wei Fan IBM T.J.Watson.
Is Random Model Better? - On its accuracy and efficiency - Wei Fan IBM T.J.Watson Joint work with Haixun Wang, Philip S. Yu, and Sheng Ma.
Lecture 4. Linear Models for Regression. Outline Linear Regression Least Square Solution Subset Least Square subset selection/forward/backward Penalized.
Support vector machine
Jason svm tutorial
Personalized Search Result Diversification via Structured Learning SHANGSONG LIANG, ZHAOCHUN REN, MAARTEN DE RIJKE UNIVERSITY OF AMSTERDAM PRESENTED BY.
Kernel – Based Methods Presented by Jason Friedman Lena Gorelick Advanced Topics in Computer and Human Vision Spring 2003.
Efficient Regression in Metric Spaces via Approximate Lipschitz Extension Lee-Ad GottliebAriel University Aryeh KontorovichBen-Gurion University Robert.
Soft Margin Estimation for Speech Recognition Main Reference: Jinyu Li, " SOFT MARGIN ESTIMATION FOR AUTOMATIC SPEECH RECOGNITION," PhD thesis, Georgia.