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» Boosting with Diverse Base Classifiers
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ECCV
2010
Springer
14 years 9 months ago
MIForests: Multiple-Instance Learning with Randomized Trees
Abstract. Multiple-instance learning (MIL) allows for training classifiers from ambiguously labeled data. In computer vision, this learning paradigm has been recently used in many ...
Christian Leistner, Amir Saffari, Horst Bischof
BMCBI
2010
100views more  BMCBI 2010»
14 years 9 months ago
Using genomic signatures for HIV-1 sub-typing
Background: Human Immunodeficiency Virus type 1 (HIV-1), the causative agent of Acquired Immune Deficiency Syndrome (AIDS), exhibits very high genetic diversity with different var...
Aridaman Pandit, Somdatta Sinha
BMCBI
2010
138views more  BMCBI 2010»
14 years 9 months ago
Amino acid classification based spectrum kernel fusion for protein subnuclear localization
Background: Prediction of protein localization in subnuclear organelles is more challenging than general protein subcelluar localization. There are only three computational models...
Suyu Mei, Wang Fei
ICSE
2004
IEEE-ACM
15 years 9 months ago
Concerning Predictability in Dependable Component-Based Systems: Classification of Quality Attributes
One of the main objectives of developing component-based software systems is to enable efficient building of systems through the integration of components. All component models def...
Ivica Crnkovic, Magnus Larsson, Otto Preiss
SE
2008
14 years 11 months ago
Collaborative Development of Knowledge Bases in Distributed Requirements Elicitation
: One of the main challenges in distributed software development is the elicitation and management of knowledge regarding system requirements. Due to spatial distribution of involv...
Steffen Lohmann, Thomas Riechert, Sören Auer