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» Learning to learn with the informative vector machine
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GECCO
2008
Springer
232views Optimization» more  GECCO 2008»
15 years 7 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...
IJCAI
1997
15 years 7 months ago
Combining Knowledge Acquisition and Machine Learning to Control Dynamic Systems
This paper presents an interactive method for building a controller for dynamic systems by using a combination of knowledge acquisition and machine learning techniques. The aim is...
G. M. Shiraz, Claude Sammut
AAAI
2008
15 years 8 months ago
Instance-level Semisupervised Multiple Instance Learning
Multiple instance learning (MIL) is a branch of machine learning that attempts to learn information from bags of instances. Many real-world applications such as localized content-...
Yangqing Jia, Changshui Zhang
MLDM
2009
Springer
15 years 10 months ago
Selection of Subsets of Ordered Features in Machine Learning
The new approach of relevant feature selection in machine learning is proposed for the case of ordered features. Feature selection and regularization of decision rule are combined ...
Oleg Seredin, Andrey Kopylov, Vadim Mottl
WSOM
2009
Springer
16 years 22 days ago
Incremental Figure-Ground Segmentation Using Localized Adaptive Metrics in LVQ
Vector quantization methods are confronted with a model selection problem, namely the number of prototypical feature representatives to model each class. In this paper we present a...
Alexander Denecke, Heiko Wersing, Jochen J. Steil,...