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111
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AAMAS
2007
Springer
15 years 3 months ago
Parallel Reinforcement Learning with Linear Function Approximation
In this paper, we investigate the use of parallelization in reinforcement learning (RL), with the goal of learning optimal policies for single-agent RL problems more quickly by us...
Matthew Grounds, Daniel Kudenko
208
Voted
IR
2011
14 years 10 months ago
Learning to rank for why-question answering
In this paper, we evaluate a number of machine learning techniques for the task of ranking answers to why-questions. We use TF-IDF together with a set of 36 linguistically motivate...
Suzan Verberne, Hans van Halteren, Daphne Theijsse...
253
Voted
AIED
2011
Springer
14 years 7 months ago
Self-assessment of Motivation: Explicit and Implicit Indicators in L2 Vocabulary Learning
Self-assessment motivation questionnaires have been used in classrooms yet many researchers find only a weak correlation between answers to these questions and learning. In this pa...
Kevin Dela Rosa, Maxine Eskenazi
191
Voted
JMLR
2012
13 years 6 months ago
Algorithms for Learning Kernels Based on Centered Alignment
This paper presents new and effective algorithms for learning kernels. In particular, as shown by our empirical results, these algorithms consistently outperform the so-called uni...
Corinna Cortes, Mehryar Mohri, Afshin Rostamizadeh
139
Voted
CP
2007
Springer
15 years 9 months ago
Limitations of Restricted Branching in Clause Learning
The techniques for making decisions, i.e., branching, play a central role in complete methods for solving structured CSP instances. In practice, there are cases when SAT solvers be...
Matti Järvisalo, Tommi A. Junttila