Max-Margin Markov Networks

In typical classification tasks, we seek a function which assigns a label to a single object. Kernel-based approaches, such as support vector machines (SVMs), which maximize the margin of confidence of the classifier, are the method of choice for many such tasks. Their popularity stems both from the ability to use high-dimensional feature spaces, and from their strong theoretical guarantees. However, many real-world tasks involve sequential, spatial, or structured data, where multiple labels must be assigned. Existing kernel-based methods ignore structure in the problem, assigning labels independently to each object, losing much useful information. Conversely, probabilistic graphical models, such as Markov networks, can represent correlations between labels, by exploiting problem structure, but cannot handle high-dimensional feature spaces, and lack strong theoretical generalization guarantees. In this paper, we present a new framework that combines the advantages of both approache...

• Type: Conference
• Year: 2003
• Where: NIPS
• Authors: Benjamin Taskar, Carlos Guestrin, Daphne Koller
• Keywords: NIPS 2003, NIPS 2007, Strong Theoretical Guarantees, Structured Data, Typical Classification Tasks