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ACL
2003
13 years 11 months ago
Using Predicate-Argument Structures for Information Extraction
In this paper we present a novel, customizable IE paradigm that takes advantage of predicate-argument structures. We also introduce a new way of automatically identifying predicat...
Mihai Surdeanu, Sanda M. Harabagiu, John Williams,...
ML
2010
ACM
159views Machine Learning» more  ML 2010»
13 years 8 months ago
Algorithms for optimal dyadic decision trees
Abstract A dynamic programming algorithm for constructing optimal dyadic decision trees was recently introduced, analyzed, and shown to be very effective for low dimensional data ...
Don R. Hush, Reid B. Porter
TSP
2010
13 years 4 months ago
Learning graphical models for hypothesis testing and classification
Sparse graphical models have proven to be a flexible class of multivariate probability models for approximating high-dimensional distributions. In this paper, we propose techniques...
Vincent Y. F. Tan, Sujay Sanghavi, John W. Fisher ...
ICML
2007
IEEE
14 years 10 months ago
Hierarchical Gaussian process latent variable models
The Gaussian process latent variable model (GP-LVM) is a powerful approach for probabilistic modelling of high dimensional data through dimensional reduction. In this paper we ext...
Neil D. Lawrence, Andrew J. Moore
ICML
2004
IEEE
14 years 10 months ago
A hierarchical method for multi-class support vector machines
We introduce a framework, which we call Divide-by-2 (DB2), for extending support vector machines (SVM) to multi-class problems. DB2 offers an alternative to the standard one-again...
Volkan Vural, Jennifer G. Dy