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» A Framework for Multiple-Instance Learning
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NIPS
2001
13 years 10 months ago
Linking Motor Learning to Function Approximation: Learning in an Unlearnable Force Field
Reaching movements require the brain to generate motor commands that rely on an internal model of the task's dynamics. Here we consider the errors that subjects make early in...
O. Donchin, Reza Shadmehr
SIGGRAPH
2010
ACM
14 years 1 months ago
Learning behavior styles with inverse reinforcement learning
We present a method for inferring the behavior styles of character controllers from a small set of examples. We show that a rich set of behavior variations can be captured by dete...
Seong Jae Lee, Zoran Popovic
ICML
1994
IEEE
14 years 20 days ago
Learning Without State-Estimation in Partially Observable Markovian Decision Processes
Reinforcement learning (RL) algorithms provide a sound theoretical basis for building learning control architectures for embedded agents. Unfortunately all of the theory and much ...
Satinder P. Singh, Tommi Jaakkola, Michael I. Jord...
CIKM
2008
Springer
13 years 11 months ago
Metric-based ontology learning
Ontology learning is an important task in Artificial Intelligence, Semantic Web and Text Mining. This paper presents a novel framework for, and solutions to, three practical probl...
Hui Yang, Jamie Callan
ICDAR
2009
IEEE
13 years 7 months ago
Using Kernel Density Classifier with Topic Model and Cost Sensitive Learning for Automatic Text Categorization
This paper proposes a novel framework for automatic text categorization problem based on the kernel density classifier. The overall goal is to tackle two main issues in automatic ...
Dwi Sianto Mansjur, Ted S. Wada, Biing-Hwang Juang