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» Practical Preference Relations for Large Data Sets
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CVPR
2008
IEEE
16 years 8 months ago
Semi-supervised learning of multi-factor models for face de-identification
With the emergence of new applications centered around the sharing of image data, questions concerning the protection of the privacy of people visible in the scene arise. Recently...
Ralph Gross, Latanya Sweeney, Fernando De la Torre...
PKDD
2009
Springer
148views Data Mining» more  PKDD 2009»
16 years 24 days ago
Feature Selection by Transfer Learning with Linear Regularized Models
Abstract. This paper presents a novel feature selection method for classification of high dimensional data, such as those produced by microarrays. It includes a partial supervisio...
Thibault Helleputte, Pierre Dupont
PVLDB
2008
138views more  PVLDB 2008»
15 years 5 months ago
A skip-list approach for efficiently processing forecasting queries
Time series data is common in many settings including scientific and financial applications. In these applications, the amount of data is often very large. We seek to support pred...
Tingjian Ge, Stanley B. Zdonik
CINQ
2004
Springer
125views Database» more  CINQ 2004»
15 years 11 months ago
Deducing Bounds on the Support of Itemsets
Mining Frequent Itemsets is the core operation of many data mining algorithms. This operation however, is very data intensive and sometimes produces a prohibitively large output. I...
Toon Calders
AAAI
1998
15 years 7 months ago
Boosting in the Limit: Maximizing the Margin of Learned Ensembles
The "minimum margin" of an ensemble classifier on a given training set is, roughly speaking, the smallest vote it gives to any correct training label. Recent work has sh...
Adam J. Grove, Dale Schuurmans