Generic ensemble methods can achieve excellent learning performance, but are not good candidates for active learning because of their different design purposes. We investigate how...
The prevailing approach to evaluating classifiers in the machine learning community involves comparing the performance of several algorithms over a series of usually unrelated data...
In a Model-Driven Development context (MDE), model transformations allow memorizing and reusing design know-how, and thus automate parts of the design and refinement steps of a so...
Erwan Brottier, Franck Fleurey, Jim Steel, Benoit ...
Computational diagnosis of cancer is a classification problem, and it has two special requirements on a learning algorithm: perfect accuracy and small number of features used in t...
Mining frequent structural patterns from graph databases is an interesting problem with broad applications. Most of the previous studies focus on pruning unfruitful search subspac...
Chen Wang, Wei Wang 0009, Jian Pei, Yongtai Zhu, B...