PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Quantity makes quality: learning with partial views
Nicolò Cesa-Bianchi, Shai Shalev-Shwartz and Ohad Shamir
In: AAAI 2011 (Nectar Program)(2011).

Abstract

In many real world applications, the number of examples to learn from is plentiful, but we can only obtain limited information on each individual example. We study the possibilities of efficient, provably correct, large-scale learning in such settings. The main theme we would like to establish is that large amounts of examples can compensate for the lack of full information on each individual example. The type of partial information we consider can be due to inherent noise or from constraints on the type of interaction with the data source. In particular, we describe and analyze algorithms for budgeted learning, in which the learner can only view a few attributes of each training example, and algorithms for learning kernel-based predictors, when individual examples are corrupted by random noise.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:9196
Deposited By:Nicolò Cesa-Bianchi
Deposited On:21 February 2012