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» Making inferences with small numbers of training sets
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CIKM
2009
Springer
14 years 3 months ago
Feature selection for ranking using boosted trees
Modern search engines have to be fast to satisfy users, so there are hard back-end latency requirements. The set of features useful for search ranking functions, though, continues...
Feng Pan, Tim Converse, David Ahn, Franco Salvetti...
WSDM
2010
ACM
266views Data Mining» more  WSDM 2010»
14 years 6 months ago
Gathering and Ranking Photos of Named Entities with High Precision, High Recall, and Diversity
Knowledge-sharing communities like Wikipedia and automated extraction methods like those of DBpedia enable the construction of large machine-processible knowledge bases with relat...
Bilyana Taneva, Mouna Kacimi, Gerhard Weikum
FOCS
2006
IEEE
14 years 2 months ago
Hardness of Learning Halfspaces with Noise
Learning an unknown halfspace (also called a perceptron) from labeled examples is one of the classic problems in machine learning. In the noise-free case, when a halfspace consist...
Venkatesan Guruswami, Prasad Raghavendra
BMCBI
2010
118views more  BMCBI 2010»
13 years 9 months ago
Walk-weighted subsequence kernels for protein-protein interaction extraction
Background: The construction of interaction networks between proteins is central to understanding the underlying biological processes. However, since many useful relations are exc...
Seonho Kim, Juntae Yoon, Jihoon Yang, Seog Park
JMLR
2012
11 years 11 months ago
Random Search for Hyper-Parameter Optimization
Grid search and manual search are the most widely used strategies for hyper-parameter optimization. This paper shows empirically and theoretically that randomly chosen trials are ...
James Bergstra, Yoshua Bengio