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» A supervised learning approach for imbalanced data sets
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KDD
2010
ACM
279views Data Mining» more  KDD 2010»
15 years 8 months ago
Unifying dependent clustering and disparate clustering for non-homogeneous data
Modern data mining settings involve a combination of attributevalued descriptors over entities as well as specified relationships between these entities. We present an approach t...
M. Shahriar Hossain, Satish Tadepalli, Layne T. Wa...
WWW
2002
ACM
16 years 5 months ago
Learning to map between ontologies on the semantic web
Ontologies play a prominent role on the Semantic Web. They make possible the widespread publication of machine understandable data, opening myriad opportunities for automated info...
AnHai Doan, Jayant Madhavan, Pedro Domingos, Alon ...
CSB
2004
IEEE
131views Bioinformatics» more  CSB 2004»
15 years 8 months ago
Improved Fourier Transform Method for Unsupervised Cell-Cycle Regulated Gene Prediction
Motivation: Cell-cycle regulated gene prediction using microarray time-course measurements of the mRNA expression levels of genes has been used by several researchers. The popular...
Karuturi R. Krishna Murthy, Liu Jian Hua
ECCV
2010
Springer
15 years 4 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
CVPR
2008
IEEE
16 years 6 months ago
Conditional density learning via regression with application to deformable shape segmentation
Many vision problems can be cast as optimizing the conditional probability density function p(C|I) where I is an image and C is a vector of model parameters describing the image. ...
Jingdan Zhang, Shaohua Kevin Zhou, Dorin Comaniciu...