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» Understanding web search via a learning paradigm
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WWW
2011
ACM
14 years 4 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...
COMPSYSTECH
2007
15 years 1 months ago
Searching the internet for learning materials through didactic indicators
: Internet offers a huge amount of didactic materials that can be used in creating new online courses. However, those materials need a deep analysis to understand their context and...
Marco Alfano, Biagio Lenzitti
ECTEL
2007
Springer
15 years 3 months ago
Enabling Project-Centred Learning through Flexible Processes: the COOPER Experience
This paper proposes a model-driven, extensible platform, delivered on the Web, which is able to support long-distance collaboration of students’ teams working on complex projects...
Stefano Ceri, Florian Daniel, Maristella Matera, A...
ICCV
2005
IEEE
15 years 3 months ago
Behaviour Understanding in Video: A Combined Method
In this paper we develop a system for human behaviour recognition in video sequences. Human behaviour is modelled as a stochastic sequence of actions. Actions are described by a f...
Neil Robertson, Ian D. Reid
SIGIR
2009
ACM
15 years 4 months ago
Smoothing clickthrough data for web search ranking
Incorporating features extracted from clickthrough data (called clickthrough features) has been demonstrated to significantly improve the performance of ranking models for Web sea...
Jianfeng Gao, Wei Yuan, Xiao Li, Kefeng Deng, Jian...