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ICONIP
1998
13 years 6 months ago
Inducing Relational Concepts with Neural Networks via the LINUS System
This paper presents a method to induce relational concepts with neural networks using the inductive logic programming system LINUS. Some first-order inductive learning tasks taken...
Rodrigo Basilio, Gerson Zaverucha, Artur S. d'Avil...
SOFSEM
1999
Springer
13 years 9 months ago
Coherent Concepts, Robust Learning
We study learning scenarios in which multiple learners are involved and “nature” imposes some constraints that force the predictions of these learners to behave coherently. Thi...
Dan Roth, Dmitry Zelenko
ALT
1994
Springer
13 years 8 months ago
Program Synthesis in the Presence of Infinite Number of Inaccuracies
Most studies modeling inaccurate data in Gold style learning consider cases in which the number of inaccuracies is finite. The present paper argues that this approach is not reaso...
Sanjay Jain
JMLR
2010
154views more  JMLR 2010»
12 years 11 months ago
Infinite Predictor Subspace Models for Multitask Learning
Given several related learning tasks, we propose a nonparametric Bayesian model that captures task relatedness by assuming that the task parameters (i.e., predictors) share a late...
Piyush Rai, Hal Daumé III
MIR
2005
ACM
129views Multimedia» more  MIR 2005»
13 years 10 months ago
Tracking concept drifting with an online-optimized incremental learning framework
Concept drifting is an important and challenging research issue in the field of machine learning. This paper mainly addresses the issue of semantic concept drifting in time series...
Jun Wu, Dayong Ding, Xian-Sheng Hua, Bo Zhang