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Max-margin Markov networks (2003)

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by Ben Taskar , Carlos Guestrin , Daphne Koller
Citations:315 - 7 self
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BibTeX

@INPROCEEDINGS{Taskar03max-marginmarkov,
    author = {Ben Taskar and Carlos Guestrin and Daphne Koller},
    title = {Max-margin Markov networks},
    booktitle = {},
    year = {2003},
    publisher = {MIT Press}
}

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Abstract

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 approaches: Maximum margin Markov (M 3) networks incorporate both kernels, which efficiently deal with high-dimensional features, and the ability to capture correlations in structured data. We present an efficient algorithm for learning M 3 networks based on a compact quadratic program formulation. We provide a new theoretical bound for generalization in structured domains. Experiments on the task of handwritten character recognition and collective hypertext classification demonstrate very significant gains over previous approaches. 1

Citations

6696 The Nature of Statistical Learning Theory - Vapnik - 1995
5666 Probabilistic reasoning in intelligent systems - Pearl - 1988
1548 BConditional random fields: Probabilistic models for segmenting and labeling sequence data - Lafferty, McCallum, et al.
554 Nonlinear Programming. Athena Scientific - Bertsekas - 1995
500 Probabilistic Networks and Expert Systems - Cowell, Dawid, et al. - 1999
308 Y: Generalized belief propagation - Yedidia, Freeman, et al.
276 D: Discriminative probabilistic models for relational data - Taskar, Abbeel, et al. - 2002
239 On the algorithmic implementation of multiclass kernel-based vector machines - Crammer, Singer - 2001
154 Hidden markov support vector machines - Altun, Tsochantaridis, et al. - 2003
45 Parameter estimation for statistical parsing models: Theory and practice of distribution-free methods - Collins - 2004
37 Covering number bounds of certain regularized linear function classes - Zhang
29 Using sparseness and analytic QP to speed training of support vector machines - Platt - 1999
24 A comparison of approaches to on-line handwritten character recognition - Kassel - 1995
8 Semidefinite methods for approximate inference on graphs with cycles - Wainwright, Jordan - 2003
3 Machine learning for sequential data: A review. Lecture notes in computer science - Dietterich - 2002
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