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» Learning of Boolean Functions Using Support Vector Machines
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ICML
2004
IEEE
14 years 10 months ago
Links between perceptrons, MLPs and SVMs
We propose to study links between three important classification algorithms: Perceptrons, Multi-Layer Perceptrons (MLPs) and Support Vector Machines (SVMs). We first study ways to...
Ronan Collobert, Samy Bengio
GECCO
2008
Springer
232views Optimization» more  GECCO 2008»
13 years 10 months ago
An efficient SVM-GA feature selection model for large healthcare databases
This paper presents an efficient hybrid feature selection model based on Support Vector Machine (SVM) and Genetic Algorithm (GA) for large healthcare databases. Even though SVM an...
Rick Chow, Wei Zhong, Michael Blackmon, Richard St...
AAAI
2007
13 years 11 months ago
Machine Learning for Automatic Mapping of Planetary Surfaces
We describe an application of machine learning to the problem of geomorphic mapping of planetary surfaces. Mapping landforms on planetary surfaces is an important task and the fi...
Tomasz F. Stepinski, Soumya Ghosh, Ricardo Vilalta
CIBCB
2009
IEEE
13 years 10 months ago
Application of machine learning approaches on quantitative structure activity relationships
Machine Learning techniques are successfully applied to establish quantitative relations between chemical structure and biological activity (QSAR), i.e. classify compounds as activ...
Mariusz Butkiewicz, Ralf Mueller, Danilo Selic, Er...
FLAIRS
2007
13 years 11 months ago
Combining Machine Learning with Linguistic Heuristics for Chinese Word Segmentation
This paper describes a hybrid model that combines machine learning with linguistic heuristics for integrating unknown word identification with Chinese word segmentation. The model...
Xiaofei Lu