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» UMDAs for dynamic optimization problems
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JMLR
2006
169views more  JMLR 2006»
15 years 11 days ago
Bayesian Network Learning with Parameter Constraints
The task of learning models for many real-world problems requires incorporating domain knowledge into learning algorithms, to enable accurate learning from a realistic volume of t...
Radu Stefan Niculescu, Tom M. Mitchell, R. Bharat ...
CI
2002
92views more  CI 2002»
15 years 7 days ago
Model Selection in an Information Economy: Choosing What to Learn
As online markets for the exchange of goods and services become more common, the study of markets composed at least in part of autonomous agents has taken on increasing importance...
Christopher H. Brooks, Robert S. Gazzale, Rajarshi...
PPSN
1990
Springer
15 years 4 months ago
Feature Construction for Back-Propagation
T h e ease of learning concepts f r o m examples in empirical machine learning depends on the attributes used for describing the training d a t a . We show t h a t decision-tree b...
Selwyn Piramuthu

Publication
211views
16 years 10 months ago
Congestion Avoidance in Computer Networks with A Connecitonless Network Layer: Part IV: A Selective Binary Feedback Scheme for G
With increasingly widespread use of computer networks, and the use of varied technology for the interconnection of computers, congestion is a significant problem. In this report, w...
K. Ramakrishnan and R. Jain,
96
Voted
CVPR
2001
IEEE
16 years 2 months ago
On Solving 2D and 3D Puzzles Using Curve Matching
We approach the problem of 2-D and 3-D puzzle solving by matching the geometric features of puzzle pieces three at a time. First, we define an affinity measure for a pair of piece...
Weixin Kong, Benjamin B. Kimia