Broad Area Colloquium For AI-Geometry-Graphics-Robotics-Vision
A Unified Algorithmic Framework for Online Learning
Yoram Singer, Hebrew University
October 11, 2004, 4:15PM
A unified algorithmic framework for numerous problems in online learning is
presented. The basic algorithm works by projecting an instantaneous hypothesis
onto a single hyperplane which forms the basis for the next instantaneous
hypothesis. In particular we discuss classification, regression, and uniclass
problems. The analysis is based on simple convexity properties combined with
mistake bound techniques. After describing the basic algorithmic setup we
discuss a few extensions to more complex problems. Specifically, we describe
online learning algorithms for hierarchical and multiclass categorization,
rank-ordering learning, sequence prediction, and pseudo-metric learning.
I will conclude with illustrations of a few systems developed at the Hebrew
university that employ the online projection method.
* Based on joint works with Koby Crammer (UPenn), Ofer Dekel (HUJI), Shai
Shwartz (HUJI), Joseph Keshet (HUJI), and Andrew Ng (Stanford).
About the Speaker
Professor Yoram Singer is a faculty member at the School of Computer Science and Engineering of The Hebrew University.
His work focuses on the design, analysis, and implementation of machine learning algorithms.
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