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Yonatan Amit

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3 papers

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JMLR Journal 2008 Journal Article

Online Learning of Complex Prediction Problems Using Simultaneous Projections

Yonatan Amit
Shai Shalev-Shwartz
Yoram Singer

We describe and analyze an algorithmic framework for online classification where each online trial consists of multiple prediction tasks that are tied together. We tackle the problem of updating the online predictor by defining a projection problem in which each prediction task corresponds to a single linear constraint. These constraints are tied together through a single slack parameter. We then introduce a general method for approximately solving the problem by projecting simultaneously and independently on each constraint which corresponds to a prediction sub-problem, and then averaging the individual solutions. We show that this approach constitutes a feasible, albeit not necessarily optimal, solution of the original projection problem. We derive concrete simultaneous projection schemes and analyze them in the mistake bound model. We demonstrate the power of the proposed algorithm in experiments with synthetic data and with multiclass text categorization tasks. [abs] [ pdf ][ bib ] &copy JMLR 2008. ( edit, beta )

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ICML Conference 2007 Conference Paper

Uncovering shared structures in multiclass classification

Yonatan Amit
Michael Fink 0002
Nathan Srebro
Shimon Ullman

This paper suggests a method for multiclass learning with many classes by simultaneously learning shared characteristics common to the classes, and predictors for the classes in terms of these characteristics. We cast this as a convex optimization problem, using trace-norm regularization and study gradient-based optimization both for the linear case and the kernelized setting.

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NeurIPS Conference 2006 Conference Paper

Online Classification for Complex Problems Using Simultaneous Projections

Yonatan Amit
Shai Shalev-Shwartz
Yoram Singer

We describe and analyze an algorithmic framework for online classiﬁcation where each online trial consists of multiple prediction tasks that are tied together. We tackle the problem of updating the online hypothesis by deﬁning a projection problem in which each prediction task corresponds to a single linear constraint. These constraints are tied together through a single slack parameter. We then in- troduce a general method for approximately solving the problem by projecting simultaneously and independently on each constraint which corresponds to a pre- diction sub-problem, and then averaging the individual solutions. We show that this approach constitutes a feasible, albeit not necessarily optimal, solution for the original projection problem. We derive concrete simultaneous projection schemes and analyze them in the mistake bound model. We demonstrate the power of the proposed algorithm in experiments with online multiclass text categorization. Our experiments indicate that a combination of class-dependent features with the simultaneous projection method outperforms previously studied algorithms.

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