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» How should data structures and algorithms be taught
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ICML
2005
IEEE
15 years 10 months ago
Learning class-discriminative dynamic Bayesian networks
In many domains, a Bayesian network's topological structure is not known a priori and must be inferred from data. This requires a scoring function to measure how well a propo...
John Burge, Terran Lane
SYNTHESE
2011
79views more  SYNTHESE 2011»
14 years 4 months ago
Underdetermination, realism and empirical equivalence
Are theories ‘underdetermined by the evidence’ in any way that should worry the scientific realist? I argue that no convincing reason has been given for thinking so. A crucial ...
John Worrall
SIGMOD
2001
ACM
200views Database» more  SIGMOD 2001»
15 years 10 months ago
Data Bubbles: Quality Preserving Performance Boosting for Hierarchical Clustering
In this paper, we investigate how to scale hierarchical clustering methods (such as OPTICS) to extremely large databases by utilizing data compression methods (such as BIRCH or ra...
Markus M. Breunig, Hans-Peter Kriegel, Peer Kr&oum...
CIKM
2009
Springer
15 years 4 months ago
Evaluating top-k queries over incomplete data streams
We study the problem of continuous monitoring of top-k queries over multiple non-synchronized streams. Assuming a sliding window model, this general problem has been a well addres...
Parisa Haghani, Sebastian Michel, Karl Aberer

Lecture Notes
443views
16 years 7 months ago
Design and Analysis of Computer Algorithms
"This course will consist of a number of major sections. The first will be a short review of some preliminary material, including asymptotics, summations, and recurrences and ...
David M. Mount