This paper presents an algorithm for inferring a Structured Hidden Markov Model (S-HMM) from a set of sequences. The S-HMMs are a sub-class of the Hierarchical Hidden Markov Model...
In order to evaluate ontology matching algorithms it is necessary to confront them with test ontologies and to compare the results with some reference. The most prominent comparis...
We propose a novel variant of the conjugate gradient algorithm, Kernel Conjugate Gradient (KCG), designed to speed up learning for kernel machines with differentiable loss functio...
Few existing argumentation frameworks are designed to deal with probabilistic knowledge, and none are designed to represent possibilistic knowledge, making them unsuitable for man...
We investigate three parameterized algorithmic schemes for graphical models that can accommodate trade-offs between time and space: 1) AND/OR Adaptive Caching (AOC(i)); 2) Variabl...
We present a fully connectionist system for the learning of first-order logic programs and the generation of corresponding models: Given a program and a set of training examples,...
We present a general machine learning framework for modelling the phenomenon of missing information in data. We propose a masking process model to capture the stochastic nature of...
In this paper, we describe our entrant in the travel division of the 2006 Trading Agent Competition (TAC). At a high level, the design of many successful autonomous trading agents...
Seong Jae Lee, Amy R. Greenwald, Victor Naroditski...
In this paper, we present a simple yet novel method of exploiting unlabeled text to further improve the accuracy of a high-performance state-of-theart named entity recognition (NE...