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ISNN
2007
Springer
15 years 7 months ago
A Hierarchical Self-organizing Associative Memory for Machine Learning
This paper proposes novel hierarchical self-organizing associative memory architecture for machine learning. This memory architecture is characterized with sparse and local interco...
Janusz A. Starzyk, Haibo He, Yue Li
SGAI
2007
Springer
15 years 7 months ago
Learning Sets of Sub-Models for Spatio-Temporal Prediction
In this paper we describe a novel technique which implements a spatiotemporal model as a set of sub-models based on first order logic. These sub-models model different, typicall...
Andrew Bennett, Derek R. Magee
ECML
1993
Springer
15 years 5 months ago
Exploiting Context When Learning to Classify
This paper addresses the problem of classifying observations when features are context-sensitive, specifically when the testing set involves a context that is different from the t...
Peter D. Turney
FLAIRS
2008
15 years 3 months ago
Learning Dynamic Naive Bayesian Classifiers
Hidden Markov models are a powerful technique to model and classify temporal sequences, such as in speech and gesture recognition. However, defining these models is still an art: ...
Miriam Martínez, Luis Enrique Sucar
AAAI
2010
15 years 2 months ago
Learning Causal Models of Relational Domains
Methods for discovering causal knowledge from observational data have been a persistent topic of AI research for several decades. Essentially all of this work focuses on knowledge...
Marc Maier, Brian Taylor, Huseyin Oktay, David Jen...