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» Learning Process Models with Missing Data
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117
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ICML
2002
IEEE
16 years 3 months ago
Pruning Improves Heuristic Search for Cost-Sensitive Learning
This paper addresses cost-sensitive classification in the setting where there are costs for measuring each attribute as well as costs for misclassification errors. We show how to ...
Valentina Bayer Zubek, Thomas G. Dietterich
106
Voted
WSC
1998
15 years 3 months ago
Customer Interfacing - Lessons Learned
Customer interfacing is the process through which one works with and relates to their customer. Often in the technical world, modelers find it easier to interface with computers r...
David M. Ferrin, Richard P. Lavecchia
115
Voted
EPIA
2003
Springer
15 years 7 months ago
Adaptation to Drifting Concepts
Most of supervised learning algorithms assume the stability of the target concept over time. Nevertheless in many real-user modeling systems, where the data is collected over an ex...
Gladys Castillo, João Gama, Pedro Medas
121
Voted
ICDM
2009
IEEE
142views Data Mining» more  ICDM 2009»
15 years 10 days ago
Building Classifiers with Independency Constraints
In this paper we study the problem of classifier learning where the input data contains unjustified dependencies between some data attributes and the class label. Such cases arise...
Toon Calders, Faisal Kamiran, Mykola Pechenizkiy
KDD
2007
ACM
167views Data Mining» more  KDD 2007»
16 years 3 months ago
Multiscale topic tomography
Modeling the evolution of topics with time is of great value in automatic summarization and analysis of large document collections. In this work, we propose a new probabilistic gr...
Ramesh Nallapati, Susan Ditmore, John D. Lafferty,...