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» An Instance Selection Approach to Multiple Instance Learning
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PKDD
2005
Springer
101views Data Mining» more  PKDD 2005»
15 years 3 months ago
A Random Method for Quantifying Changing Distributions in Data Streams
In applications such as fraud and intrusion detection, it is of great interest to measure the evolving trends in the data. We consider the problem of quantifying changes between tw...
Haixun Wang, Jian Pei
JMLR
2012
13 years 9 days ago
Multi-label Subspace Ensemble
A challenging problem of multi-label learning is that both the label space and the model complexity will grow rapidly with the increase in the number of labels, and thus makes the...
Tianyi Zhou, Dacheng Tao
JMLR
2011
111views more  JMLR 2011»
14 years 4 months ago
Models of Cooperative Teaching and Learning
While most supervised machine learning models assume that training examples are sampled at random or adversarially, this article is concerned with models of learning from a cooper...
Sandra Zilles, Steffen Lange, Robert Holte, Martin...
ICML
2009
IEEE
15 years 4 months ago
Non-monotonic feature selection
We consider the problem of selecting a subset of m most informative features where m is the number of required features. This feature selection problem is essentially a combinator...
Zenglin Xu, Rong Jin, Jieping Ye, Michael R. Lyu, ...
JMLR
2006
109views more  JMLR 2006»
14 years 9 months ago
Some Discriminant-Based PAC Algorithms
A classical approach in multi-class pattern classification is the following. Estimate probability distributions that generated the observations for each label class, and then labe...
Paul W. Goldberg