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IJCAI
1993
13 years 9 months ago
Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning
Since most real-world applications of classification learning involve continuous-valued attributes, properly addressing the discretization process is an important problem. This pa...
Usama M. Fayyad, Keki B. Irani
ISCI
1998
139views more  ISCI 1998»
13 years 7 months ago
A Rough Set Approach to Attribute Generalization in Data Mining
This paper presents a method for updating approximations of a concept incrementally. The results can be used to implement a quasi-incremental algorithm for learning classification...
Chien-Chung Chan
CVPR
2009
IEEE
15 years 2 months ago
Learning To Detect Unseen Object Classes by Between-Class Attribute Transfer
We study the problem of object classification when training and test classes are disjoint, i.e. no training examples of the target classes are available. This setup has hardly be...
Christoph H. Lampert, Hannes Nickisch, Stefan Harm...
CORR
2010
Springer
146views Education» more  CORR 2010»
13 years 7 months ago
Active Learning for Hidden Attributes in Networks
In many networks, vertices have hidden attributes that are correlated with the network's topology. For instance, in social networks, people are more likely to be friends if t...
Xiaoran Yan, Yaojia Zhu, Jean-Baptiste Rouquier, C...
ICML
2010
IEEE
13 years 8 months ago
Efficient Learning with Partially Observed Attributes
We describe and analyze efficient algorithms for learning a linear predictor from examples when the learner can only view a few attributes of each training example. This is the ca...
Nicolò Cesa-Bianchi, Shai Shalev-Shwartz, O...