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ICML
2000
IEEE
16 years 20 days ago
Learning Subjective Functions with Large Margins
In manyoptimization and decision problems the objective function can be expressed as a linear combinationof competingcriteria, the weights of whichspecify the relative importanceo...
Claude-Nicolas Fiechter, Seth Rogers
ECML
2007
Springer
15 years 6 months ago
An Unsupervised Learning Algorithm for Rank Aggregation
Many applications in information retrieval, natural language processing, data mining, and related fields require a ranking of instances with respect to a specified criteria as op...
Alexandre Klementiev, Dan Roth, Kevin Small
AIED
2005
Springer
15 years 5 months ago
Making Learning Design Standards Work with an Ontology of Educational Theories
In this paper, we present an ontology of educational theories their relation to learning design. This ontology takes into account learning design (LD) specifications such as OUNL-E...
Valéry Psyché, Jacqueline Bourdeau, ...
MHCI
2005
Springer
15 years 5 months ago
Field-based mLearning: who wants what?
The real needs of end users of software technology are often neglected until it is too late. An approach is outlined that puts people before technology, and investigates the real ...
Paul Ryan, Enda Finn
ML
1998
ACM
113views Machine Learning» more  ML 1998»
14 years 11 months ago
Machine Learning for the Detection of Oil Spills in Satellite Radar Images
During a project examining the use of machine learning techniques for oil spill detection, we have encountered several essential questions that we believe deserve the attention of ...
Miroslav Kubat, Robert C. Holte, Stan Matwin