PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Efficient Learning with Partially Observed Attributes
Nicolò Cesa-Bianchi, Shai Shalev-Shwartz and Ohad Shamir
In: ICML 2010(2010).

Abstract

We describe and analyze ecient algorithms for learning a linear predictor from examples when the learner can only view a few attributes of each training example. This is the case, for instance, in medical research, where each patient participating in the experiment is only willing to go through a small number of tests. Our analysis bounds the number of additional examples sucient to compensate for the lack of full information on each training example. We demonstrate the ef- ciency of our algorithms by showing that when running on digit recognition data, they obtain a high prediction accuracy even when the learner gets to see only four pixels of each image.

PDF - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:6939
Deposited By:Ohad Shamir
Deposited On:01 June 2010