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ICML
2009
IEEE
16 years 2 months ago
A stochastic memoizer for sequence data
We propose an unbounded-depth, hierarchical, Bayesian nonparametric model for discrete sequence data. This model can be estimated from a single training sequence, yet shares stati...
Frank Wood, Cédric Archambeau, Jan Gasthaus...
AAAI
2004
15 years 2 months ago
Learning and Applying Competitive Strategies
Learning reusable sequences can support the development of expertise in many domains, either by improving decisionmaking quality or decreasing execution speed. This paper introduc...
Esther Lock, Susan L. Epstein
PODS
2012
ACM
281views Database» more  PODS 2012»
13 years 3 months ago
Mergeable summaries
We study the mergeability of data summaries. Informally speaking, mergeability requires that, given two summaries on two data sets, there is a way to merge the two summaries into ...
Pankaj K. Agarwal, Graham Cormode, Zengfeng Huang,...
138
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ATAL
2005
Springer
15 years 7 months ago
Kaa: policy-based explorations of a richer model for adjustable autonomy
Though adjustable autonomy is hardly a new topic in agent systems, there has been a general lack of consensus on terminology and basic concepts. In this paper, we describe the mul...
Jeffrey M. Bradshaw, Hyuckchul Jung, Shriniwas Kul...
KDD
2009
ACM
269views Data Mining» more  KDD 2009»
16 years 2 months ago
Extracting discriminative concepts for domain adaptation in text mining
One common predictive modeling challenge occurs in text mining problems is that the training data and the operational (testing) data are drawn from different underlying distributi...
Bo Chen, Wai Lam, Ivor Tsang, Tak-Lam Wong