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» Learning with the Set Covering Machine
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HAIS
2009
Springer
13 years 11 months ago
Beyond Homemade Artificial Data Sets
One of the most important challenges in supervised learning is how to evaluate the quality of the models evolved by different machine learning techniques. Up to now, we have relied...
Núria Macià, Albert Orriols-Puig, Es...
LREC
2010
156views Education» more  LREC 2010»
13 years 9 months ago
Learning Subjectivity Phrases missing from Resources through a Large Set of Semantic Tests
In recent years, blogs and social networks have particularly boosted interests for opinion mining research. In order to satisfy real-scale applicative needs, a main task is to cre...
Matthieu Vernier, Laura Monceaux, Béatrice ...
ICML
1998
IEEE
14 years 8 months ago
A Fast, Bottom-Up Decision Tree Pruning Algorithm with Near-Optimal Generalization
In this work, we present a new bottom-up algorithmfor decision tree pruning that is very e cient requiring only a single pass through the given tree, and prove a strong performanc...
Michael J. Kearns, Yishay Mansour
NIPS
2003
13 years 9 months ago
Boosting versus Covering
We investigate improvements of AdaBoost that can exploit the fact that the weak hypotheses are one-sided, i.e. either all its positive (or negative) predictions are correct. In pa...
Kohei Hatano, Manfred K. Warmuth
CORR
2010
Springer
149views Education» more  CORR 2010»
13 years 7 months ago
Using Rough Set and Support Vector Machine for Network Intrusion Detection
The main function of IDS (Intrusion Detection System) is to protect the system, analyze and predict the behaviors of users. Then these behaviors will be considered an attack or a ...
Rung Ching Chen, Kai-Fan Cheng, Chia-Fen Hsieh