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IADIS
2008
13 years 9 months ago
Modelling Collaborative Competence Level Using Machine Learning Techniques
Using open e-learning platforms as a tool to support the learning process has become an international tendency. Specially, in order to motivate the achievement of desired competen...
Laura Mancera Valetts, Silvia Baldiris Navarro, Ra...
JMLR
2008
123views more  JMLR 2008»
13 years 7 months ago
Optimization Techniques for Semi-Supervised Support Vector Machines
Due to its wide applicability, the problem of semi-supervised classification is attracting increasing attention in machine learning. Semi-Supervised Support Vector Machines (S3VMs...
Olivier Chapelle, Vikas Sindhwani, S. Sathiya Keer...
MICAI
2010
Springer
13 years 5 months ago
Supervised Machine Learning for Predicting the Meaning of Verb-Noun Combinations in Spanish
The meaning of such verb-noun combinations as take care, undertake work, pay attention can be generalized as DO what is designated by the noun. Likewise, the meaning of make a deci...
Olga Kolesnikova, Alexander F. Gelbukh
PVM
2005
Springer
14 years 28 days ago
Some Improvements to a Parallel Decomposition Technique for Training Support Vector Machines
We consider a parallel decomposition technique for solving the large quadratic programs arising in training the learning methodology Support Vector Machine. At each iteration of th...
Thomas Serafini, Luca Zanni, Gaetano Zanghirati
ICML
1999
IEEE
14 years 8 months ago
Lazy Bayesian Rules: A Lazy Semi-Naive Bayesian Learning Technique Competitive to Boosting Decision Trees
Lbr is a lazy semi-naive Bayesian classi er learning technique, designed to alleviate the attribute interdependence problem of naive Bayesian classi cation. To classify a test exa...
Zijian Zheng, Geoffrey I. Webb, Kai Ming Ting