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Singer, Yoram - School of Computer Science and Engineering, Hebrew University of Jerusalem
On the Equivalence of Weak Learnability and Linear Separability: New Relaxations and Efficient Boosting Algorithms
Journal of Computer and System Sciences 56, 133152 (1998) On the Learnability and Usage of Acyclic Probabilistic
Mathematical Programming manuscript No. (will be inserted by the editor)
THE FORGETRON: A KERNEL-BASED PERCEPTRON ON A OFER DEKEL, SHAI SHALEV-SHWARTZ, AND YORAM SINGER
International Journal of Neural Systems, Vol. 8, No. 4 (August, 1997) 445455 Special Issue on Data Mining in Finance
Online Ranking by Projecting Koby Crammer
Cybernetics c Springer-Verlag 1994
Spikernels: Predicting Arm Movements by Embedding Population Spike Rate Patterns in Inner-Product Spaces
A Large Margin Algorithm for Speech-to-Phoneme and Music-to-Score Alignment
Mathematical Finance, Vol. 8, No. 4 (October 1998), 325347 ON-LINE PORTFOLIO SELECTION USING MULTIPLICATIVE UPDATES
Adaptive Mixtures of Probabilistic Transducers Yoram Singer
Efficient Projections onto the 1-Ball for Learning in High Dimensions John Duchi JDUCHI@CS.STANFORD.EDU
Composite Objective Mirror Descent UC Berkeley
Adaptive Subgradient Methods Adaptive Subgradient Methods for
A Coordinate Descent Algorithm for Learning Compact Ranking Functions
Individual Sequence Prediction using Memory-efficient Context Trees
Boosting with Structural Sparsity John Duchi jduchi@cs.berkeley.edu
Entire Relaxation Path for Maximum Entropy Problems Moshe Dubiner
