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ACMSE
2010
ACM
12 years 11 months ago
Learning to rank using 1-norm regularization and convex hull reduction
The ranking problem appears in many areas of study such as customer rating, social science, economics, and information retrieval. Ranking can be formulated as a classification pro...
Xiaofei Nan, Yixin Chen, Xin Dang, Dawn Wilkins
CIKM
2009
Springer
13 years 11 months ago
Learning to rank from Bayesian decision inference
Ranking is a key problem in many information retrieval (IR) applications, such as document retrieval and collaborative filtering. In this paper, we address the issue of learning ...
Jen-Wei Kuo, Pu-Jen Cheng, Hsin-Min Wang
WSDM
2009
ACM
125views Data Mining» more  WSDM 2009»
13 years 11 months ago
Less is more: sampling the neighborhood graph makes SALSA better and faster
In this paper, we attempt to improve the effectiveness and the efficiency of query-dependent link-based ranking algorithms such as HITS, MAX and SALSA. All these ranking algorith...
Marc Najork, Sreenivas Gollapudi, Rina Panigrahy
DOCENG
2006
ACM
13 years 10 months ago
NEWPAR: an automatic feature selection and weighting schema for category ranking
Category ranking provides a way to classify plain text documents into a pre-determined set of categories. This work proposes to have a look at typical document collections and ana...
Fernando Ruiz-Rico, José Luis Vicedo Gonz&a...
ICDM
2007
IEEE
248views Data Mining» more  ICDM 2007»
13 years 8 months ago
Adapting SVM Classifiers to Data with Shifted Distributions
Many data mining applications can benefit from adapting existing classifiers to new data with shifted distributions. In this paper, we present Adaptive Support Vector Machine (Ada...
Jun Yang 0003, Rong Yan, Alexander G. Hauptmann