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CVPR
2012
IEEE
13 years 9 months ago
Boosting algorithms for simultaneous feature extraction and selection
The problem of simultaneous feature extraction and selection, for classifier design, is considered. A new framework is proposed, based on boosting algorithms that can either 1) s...
Mohammad J. Saberian, Nuno Vasconcelos
KDD
2001
ACM
203views Data Mining» more  KDD 2001»
16 years 7 months ago
Ensemble-index: a new approach to indexing large databases
The problem of similarity search (query-by-content) has attracted much research interest. It is a difficult problem because of the inherently high dimensionality of the data. The ...
Eamonn J. Keogh, Selina Chu, Michael J. Pazzani
SIGMOD
2004
ACM
262views Database» more  SIGMOD 2004»
16 years 6 months ago
The Next Database Revolution
Database system architectures are undergoing revolutionary changes. Most importantly, algorithms and data are being unified by integrating programming languages with the database ...
Jim Gray
IDA
2002
Springer
15 years 6 months ago
Classification with sparse grids using simplicial basis functions
Recently we presented a new approach [20] to the classification problem arising in data mining. It is based on the regularization network approach but in contrast to other methods...
Jochen Garcke, Michael Griebel
WWW
2011
ACM
15 years 1 months ago
Parallel boosted regression trees for web search ranking
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. ...
Stephen Tyree, Kilian Q. Weinberger, Kunal Agrawal...