Year: 2008
Type: conference-paper
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 advantage 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 for which discriminative techniques have computational or statistical problems. 1.
Source: MPG.PuRe (Max Planck Society)
Authors: Christoph H. Lampert, Matthew B. Blaschko
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W1557115401