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» Model Adaptation via Model Interpolation and Boosting for We...
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VLDB
2008
ACM
170views Database» more  VLDB 2008»
14 years 5 months ago
A multi-ranker model for adaptive XML searching
The evolution of computing technology suggests that it has become more feasible to offer access to Web information in a ubiquitous way, through various kinds of interaction device...
Ho Lam Lau, Wilfred Ng
ICDCS
2005
IEEE
13 years 10 months ago
Using a Layered Markov Model for Distributed Web Ranking Computation
The link structure of the Web graph is used in algorithms such as Kleinberg’s HITS and Google’s PageRank to assign authoritative weights to Web pages and thus rank them. Both ...
Jie Wu, Karl Aberer
AIRWEB
2007
Springer
13 years 11 months ago
Using Spam Farm to Boost PageRank
Nowadays web spamming has emerged to take the economic advantage of high search rankings and threatened the accuracy and fairness of those rankings. Understanding spamming techniq...
Ye Du, Yaoyun Shi, Xin Zhao
WWW
2006
ACM
14 years 5 months ago
Beyond PageRank: machine learning for static ranking
Since the publication of Brin and Page's paper on PageRank, many in the Web community have depended on PageRank for the static (query-independent) ordering of Web pages. We s...
Matthew Richardson, Amit Prakash, Eric Brill
ICML
2004
IEEE
14 years 5 months ago
Training conditional random fields via gradient tree boosting
Conditional Random Fields (CRFs; Lafferty, McCallum, & Pereira, 2001) provide a flexible and powerful model for learning to assign labels to elements of sequences in such appl...
Thomas G. Dietterich, Adam Ashenfelter, Yaroslav B...