1403 search results - page 165 / 281 » Set cover algorithms for very large datasets |
146
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DATE
2004
IEEE
15 years 10 months ago
2004
IEEE
In this paper, we present a novel all-solutions preimage SAT solver, SOLALL, with the following features: (1) a new success-driven learning algorithm employing smaller cut sets; (...
163
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KDD
1999
ACM
15 years 10 months ago
1999
ACM
We propose to use AdaBoost to efficiently learn classifiers over very large and possibly distributed data sets that cannot fit into main memory, as well as on-line learning wher...
199
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KDD
2012
ACM
13 years 8 months ago
2012
ACM
An ideal outcome of pattern mining is a small set of informative patterns, containing no redundancy or noise, that identifies the key structure of the data at hand. Standard freq...
207
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WWW
2011
ACM
15 years 1 months ago
2011
ACM
Gradient Boosted Regression Trees (GBRT) are the current state-of-the-art learning paradigm for machine learned websearch ranking — a domain notorious for very large data sets. ...
192
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SIGMOD
2001
ACM
16 years 6 months ago
2001
ACM
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...