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» Machine Learning Approaches for Inducing Student Models
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ICML
2009
IEEE
14 years 9 months ago
Constraint relaxation in approximate linear programs
Approximate Linear Programming (ALP) is a reinforcement learning technique with nice theoretical properties, but it often performs poorly in practice. We identify some reasons for...
Marek Petrik, Shlomo Zilberstein
ICML
1994
IEEE
14 years 7 days ago
Combining Top-down and Bottom-up Techniques in Inductive Logic Programming
This paper describes a new methodfor inducing logic programs from examples which attempts to integrate the best aspects of existingILP methodsintoa singlecoherent framework. In pa...
John M. Zelle, Raymond J. Mooney, Joshua B. Konvis...
AIPS
2009
13 years 9 months ago
Learning User Plan Preferences Obfuscated by Feasibility Constraints
It has long been recognized that users can have complex preferences on plans. Non-intrusive learning of such preferences by observing the plans executed by the user is an attracti...
Nan Li, William Cushing, Subbarao Kambhampati, Sun...
ISMVL
1997
IEEE
134views Hardware» more  ISMVL 1997»
14 years 28 days ago
Functional Decomposition of MVL Functions Using Multi-Valued Decision Diagrams
In this paper, the minimization of incompletely specified multi-valued functions using functional decomposition is discussed. From the aspect of machine learning, learning sample...
Craig M. Files, Rolf Drechsler, Marek A. Perkowski
ICALT
2008
IEEE
14 years 3 months ago
Designing Collaborative Learning Applications
Future collaborative learning technologies are characterized by the CSCL community as highly malleable and flexible. A promising approach for meeting these expectations is to use ...
Jacques Lonchamp