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Supervised versus multiple instance learning: an empirical comparison
TLDR
We empirically study the relationship between supervised and multiple instance (MI) learning in the Multiple Instance setting. Expand
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Multiple-Instance Active Learning
TLDR
We present a framework for active learning in the multiple-instance (MI) setting. Expand
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Multiple Instance Regression
TLDR
This paper introduces multiple instance regression, a variant of multiple regression in which each data point may be described by more than one vector of values for the independent variables. Expand
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Multi-task reinforcement learning: a hierarchical Bayesian approach
TLDR
We consider the problem of multi-task reinforcement learning, where the agent needs to solve a sequence of Markov Decision Processes (MDPs) chosen randomly from a fixed but unknown distribution. Expand
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Representing Sentence Structure in Hidden Markov Models for Information Extraction
TLDR
We study the application of Hidden Markov Models (HMMs) to learning information extractors for -ary relations from free text. Expand
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Automatic discovery and transfer of MAXQ hierarchies
TLDR
We present an algorithm, HI-MAT (Hierarchy Induction via Models And Trajectories), that discovers MAXQ task hierarchies by applying dynamic Bayesian network models to a successful trajectory from a source reinforcement learning task. Expand
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Hierarchical Hidden Markov Models for Information Extraction
TLDR
We propose and evaluate an approach that is based on using hierarchical hidden Markov models to represent the grammatical structure of the sentences being processed. Expand
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A theoretical and empirical analysis of support vector machine methods for multiple-instance classification
TLDR
The standard support vector machine (SVM) formulation, widely used for supervised learning, possesses several intuitive and desirable properties. Expand
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Online Planning for Resource Production in Real-Time Strategy Games
TLDR
We develop an online planner for resource production in the RTS game of Wargus, where the preconditions and effects of the actions obey many properties that are common across RTS games. Expand
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Bayesian Hierarchical Reinforcement Learning
TLDR
We describe an approach to incorporating Bayesian priors in the MAXQ framework for hierarchical reinforcement learning (HRL). Expand
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