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» MILIS: Multiple Instance Learning with Instance Selection
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CSB
2004
IEEE
149views Bioinformatics» more  CSB 2004»
13 years 11 months ago
Weighting Features to Recognize 3D Patterns of Electron Density in X-Ray Protein Crystallography
Feature selection and weighting are central problems in pattern recognition and instance-based learning. In this work, we discuss the challenges of constructing and weighting feat...
Kreshna Gopal, Tod D. Romo, James C. Sacchettini, ...
NLPRS
2001
Springer
14 years 3 days ago
A Bayesian Approach to Semi-Supervised Learning
Recent research in automated learning has focused on algorithms that learn from a combination of tagged and untagged data. Such algorithms can be referred to as semi-supervised in...
Rebecca F. Bruce
AIME
2009
Springer
13 years 11 months ago
The Role of Biomedical Dataset in Classification
In this paper, we investigate the role of a biomedical dataset on the classification accuracy of an algorithm. We quantify the complexity of a biomedical dataset using five complex...
Ajay Kumar Tanwani, Muddassar Farooq
EWCBR
2006
Springer
13 years 11 months ago
Combining Multiple Similarity Metrics Using a Multicriteria Approach
The design of a CBR system involves the use of similarity metrics. For many applications, various functions can be adopted to compare case features and to aggregate them into a glo...
Luc Lamontagne, Irène Abi-Zeid
AUTONOMICS
2007
ACM
13 years 11 months ago
A framework to support multiple reconfiguration strategies
Self-management is a key feature of autonomic systems. This often demands the dynamic reconfiguration of a distributed application. An important issue in the reconfiguration proce...
Liliana Rosa, Luís Rodrigues, Antóni...