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FLAIRS
2007
14 years 12 months ago
Context-Sensitive MTL Networks for Machine Lifelong Learning
Context-sensitive Multiple Task Learning, or csMTL, is presented as a method of inductive transfer that uses a single output neural network and additional contextual inputs for le...
Daniel L. Silver, Ryan Poirier
IJCNN
2008
IEEE
15 years 4 months ago
Self-organizing neural models integrating rules and reinforcement learning
— Traditional approaches to integrating knowledge into neural network are concerned mainly about supervised learning. This paper presents how a family of self-organizing neural m...
Teck-Hou Teng, Zhong-Ming Tan, Ah-Hwee Tan
ICANN
2003
Springer
15 years 2 months ago
Learning Rule Representations from Boolean Data
We discuss a Probably Approximate Correct (PAC) learning paradigm for Boolean formulas, which we call PAC meditation, where the class of formulas to be learnt is not known in advan...
Bruno Apolloni, Andrea Brega, Dario Malchiodi, Gio...
ILP
1999
Springer
15 years 1 months ago
Approximate ILP Rules by Backpropagation Neural Network: A Result on Thai Character Recognition
This paper presents an application of Inductive Logic Programming (ILP) and Backpropagation Neural Network (BNN) to the problem of Thai character recognition. In such a learning pr...
Boonserm Kijsirikul, Sukree Sinthupinyo
ESANN
2006
14 years 11 months ago
Neural networks and machine learning in bioinformatics - theory and applications
Bioinformatics is a promising and innovative research field. Despite of a high number of techniques specifically dedicated to bioinformatics problems as well as many successful app...
Udo Seiffert, Barbara Hammer, Samuel Kaski, Thomas...