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KDD
2012
ACM
281views Data Mining» more  KDD 2012»
13 years 10 days ago
Active spectral clustering via iterative uncertainty reduction
Spectral clustering is a widely used method for organizing data that only relies on pairwise similarity measurements. This makes its application to non-vectorial data straightforw...
Fabian L. Wauthier, Nebojsa Jojic, Michael I. Jord...
WWW
2007
ACM
15 years 10 months ago
Learning ontologies to improve the quality of automatic web service matching
This paper presents a novel technique that significantly improves the quality of semantic Web service matching by (1) automatically generating ontologies based on Web service desc...
Hui Guo, Anca Ivan, Rama Akkiraju, Richard Goodwin
RECSYS
2009
ACM
15 years 4 months ago
Learning to recommend helpful hotel reviews
User-generated reviews are a common and valuable source of product information, yet little attention has been paid as to how best to present them to end-users. In this paper, we d...
Michael P. O'Mahony, Barry Smyth
SIGIR
2009
ACM
15 years 4 months ago
Reciprocal rank fusion outperforms condorcet and individual rank learning methods
Reciprocal Rank Fusion (RRF), a simple method for combining the document rankings from multiple IR systems, consistently yields better results than any individual system, and bett...
Gordon V. Cormack, Charles L. A. Clarke, Stefan B&...
ICCAD
2008
IEEE
107views Hardware» more  ICCAD 2008»
15 years 4 months ago
Importance sampled circuit learning ensembles for robust analog IC design
This paper presents ISCLEs, a novel and robust analog design method that promises to scale with Moore’s Law, by doing boosting-style importance sampling on digital-sized circuit...
Peng Gao, Trent McConaghy, Georges G. E. Gielen