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» Learning to Select a Ranking Function
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PICS
2003
13 years 9 months ago
Eye Tracking Observers During Rank Order, Paired Comparison, and Graphical Rating Tasks
In studying image quality and image preference it is necessary to collect psychophysical data. A variety of methods are used to arrive at interval scale values which indicate the ...
Jason S. Babcock, Jeff B. Pelz, Mark D. Fairchild
CORR
2006
Springer
178views Education» more  CORR 2006»
13 years 8 months ago
Low-rank matrix factorization with attributes
We develop a new collaborative filtering (CF) method that combines both previously known users' preferences, i.e. standard CF, as well as product/user attributes, i.e. classi...
Jacob Abernethy, Francis Bach, Theodoros Evgeniou,...
KAIS
2007
97views more  KAIS 2007»
13 years 7 months ago
Stability of feature selection algorithms: a study on high-dimensional spaces
With the proliferation of extremely high-dimensional data, feature selection algorithms have become indispensable components of the learning process. Strangely, despite extensive ...
Alexandros Kalousis, Julien Prados, Melanie Hilari...
ICML
2003
IEEE
14 years 8 months ago
Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions
An approach to semi-supervised learning is proposed that is based on a Gaussian random field model. Labeled and unlabeled data are represented as vertices in a weighted graph, wit...
Xiaojin Zhu, Zoubin Ghahramani, John D. Lafferty
ICPR
2000
IEEE
14 years 9 months ago
On Gaussian Radial Basis Function Approximations: Interpretation, Extensions, and Learning Strategies
In this paper we focus on an interpretation of Gaussian radial basis functions (GRBF) which motivates extensions and learning strategies. Specifically, we show that GRBF regressio...
Mário A. T. Figueiredo