161 search results - page 22 / 33 » A Multiple Resampling Method for Learning from Imbalanced Da... |
ICML
2006
IEEE
14 years 8 months ago
2006
IEEE
We address the problem of learning a kernel for a given supervised learning task. Our approach consists in searching within the convex hull of a prescribed set of basic kernels fo...
AAAI
2010
13 years 9 months ago
2010
Multi-label learning deals with data associated with multiple labels simultaneously. Previous work on multi-label learning assumes that for each instance, the "full" lab...
IDA
2007
Springer
13 years 7 months ago
2007
Springer
Unsupervised sequence learning is important to many applications. A learner is presented with unlabeled sequential data, and must discover sequential patterns that characterize th...
JMLR
2011
13 years 2 months ago
2011
We propose a framework MIC (Multiple Inclusion Criterion) for learning sparse models based on the information theoretic Minimum Description Length (MDL) principle. MIC provides an...
KDD
2009
ACM
14 years 8 months ago
2009
ACM
Nowadays, enormous amounts of data are continuously generated not only in massive scale, but also from different, sometimes conflicting, views. Therefore, it is important to conso...