PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Joint Kernel Support Estimation for Structured Prediction
Christoph Lampert and Matthew Blaschko
In: NIPS SISO 2008: Structured Input - Structured Output, 12 Dec 2008, Whistler, Canada.

Abstract

We present a new technique for structured prediction that works in a hybrid generative/discriminative way, using a one-class support vector machine to model the joint probability of (input, output)-pairs in a joint reproducing kernel Hilbert space. Compared to discriminative techniques, like conditional random fields or structured output SVMs, the proposed method has the ad- vantage that its training time depends only on the number of training examples, not on the size of the label space. Due to its generative aspect, it is also very tolerant against ambiguous, incomplete or incorrect labels. Experiments on realistic data show that our method works efficiently and robustly in situations that discriminative techniques have problems with or that are computationally infeasible for them.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:4819
Deposited By:Christoph Lampert
Deposited On:24 March 2009