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» Causal inference using the algorithmic Markov condition
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ML
2000
ACM
154views Machine Learning» more  ML 2000»
13 years 10 months ago
Lazy Learning of Bayesian Rules
The naive Bayesian classifier provides a simple and effective approach to classifier learning, but its attribute independence assumption is often violated in the real world. A numb...
Zijian Zheng, Geoffrey I. Webb
ICDM
2006
IEEE
91views Data Mining» more  ICDM 2006»
14 years 4 months ago
Entropy-based Concept Shift Detection
When monitoring sensory data (e.g., from a wearable device) the context oftentimes changes abruptly: people move from one situation (e.g., working quietly in their office) to ano...
Peter Vorburger, Abraham Bernstein
ACL
2008
13 years 11 months ago
Unsupervised Learning of Acoustic Sub-word Units
Accurate unsupervised learning of phonemes of a language directly from speech is demonstrated via an algorithm for joint unsupervised learning of the topology and parameters of a ...
Balakrishnan Varadarajan, Sanjeev Khudanpur, Emman...
ATAL
2005
Springer
14 years 3 months ago
Rapid on-line temporal sequence prediction by an adaptive agent
Robust sequence prediction is an essential component of an intelligent agent acting in a dynamic world. We consider the case of near-future event prediction by an online learning ...
Steven Jensen, Daniel Boley, Maria L. Gini, Paul R...
HICSS
2003
IEEE
207views Biometrics» more  HICSS 2003»
14 years 3 months ago
Formalizing Multi-Agent POMDP's in the context of network routing
This paper uses partially observable Markov decision processes (POMDP’s) as a basic framework for MultiAgent planning. We distinguish three perspectives: first one is that of a...
Bharaneedharan Rathnasabapathy, Piotr J. Gmytrasie...