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(1985). A learning algorithm for boltzmann machines. (2010). Learning the structure of deep sparse graphical models. In AI/Statistics. On tight approximate inference of the logistic-normal topic admixture model. In AI/Statistics.ference using message propoga-tion and topology transformation in vector Gaussian continuous networks. In UAI. Bayesian analysis(More)
This paper presents two new formulations of multiple-instance learning as a maximum margin problem. The proposed extensions of the Support Vector Machine (SVM) learning approach lead to mixed integer quadratic programs that can be solved heuristically. Our generalization of SVMs makes a state-of-the-art classification technique, including non-linear(More)
Learning general functional dependencies is one of the main goals in machine learning. Recent progress in kernel-based methods has focused on designing flexible and powerful input representations. This paper addresses the complementary issue of problems involving complex outputs such as multiple dependent output variables and structured output spaces. We(More)
This paper presents a novel discriminative learning technique for label sequences based on a combination of the two most successful learning algorithms, Support Vector Machines and Hidden Markov Models which we call Hidden Markov Support Vector Machine. The proposed architecture handles dependencies between neighboring labels using Viterbi decoding. In(More)
We present an approach to query expansion in answer retrieval that uses Statistical Machine Translation (SMT) techniques to bridge the lexical gap between questions and answers. SMT-based query expansion is done by i) using a full-sentence paraphraser to introduce synonyms in context of the entire query, and ii) by translating query terms into answer terms(More)
In pattern classification it is usually assumed that a training set of labeled patterns is available. Multiple-Instance Learning (MIL) generalizes this problem setting by making weaker assumptions about the labeling information. While each pattern is still believed to possess a true label, training labels are associated with sets or bags of patterns rather(More)
This paper presents a new method for topic-based document segmentation, i.e., the identification of boundaries between parts of a document that bear on different topics. The method combines the use of the Probabilistic Latent Semantic Analysis (PLSA) model with the method of selecting segmentation points based on the similarity values between pairs of(More)
Supervised learning, one of the most important areas of machine learning, is the general problem of learning a function that predicts the best value for a response variable y for an observation x by making use of a sample of input-output pairs. Traditionally, in classification, the values that y can take are simple, in the sense that they can be(More)
Polycategorical classification deals with the task of solving multiple interdependent classification problems. The key challenge is to systematically exploit possible dependencies among the labels to improve on the standard approach of solving each classification problem independently. Our method operates in two stages: the first stage uses the observed set(More)
T he focus of this work is the computation of e cient strategies for commodity trading in a multimarket environment. In today's ``global economy'' commodities are often bought in one location and then sold (right away, or after some storage period) in di erent markets. T hus, a trading decision in one location must be based on expectations about future(More)