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» Training of Support Vector Machines with Mahalanobis Kernels
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TNN
2010
234views Management» more  TNN 2010»
15 years 23 days ago
Novel maximum-margin training algorithms for supervised neural networks
This paper proposes three novel training methods, two of them based on the back-propagation approach and a third one based on information theory for Multilayer Perceptron (MLP) bin...
Oswaldo Ludwig, Urbano Nunes
IPM
2008
159views more  IPM 2008»
15 years 6 months ago
Exploring syntactic structured features over parse trees for relation extraction using kernel methods
Extracting semantic relationships between entities from text documents is challenging in information extraction and important for deep information processing and management. This ...
Min Zhang, Guodong Zhou, AiTi Aw
IJBRA
2010
133views more  IJBRA 2010»
15 years 3 months ago
Scalable biomedical Named Entity Recognition: investigation of a database-supported SVM approach
This paper explores the scalability issues associated with solving the Named Entity Recognition (NER) problem using Support Vector Machines (SVM) and high-dimensional features and ...
Mona Soliman Habib, Jugal Kalita
CVPR
2009
IEEE
15 years 10 months ago
Classification of tensors and fiber tracts using Mercer-kernels encoding soft probabilistic spatial and diffusion information
In this paper, we present a kernel-based approach to the clustering of diffusion tensors and fiber tracts. We propose to use a Mercer kernel over the tensor space where both spati...
Radhouène Neji, Nikos Paragios, Gilles Fleu...
FLAIRS
2004
15 years 7 months ago
Gene Expression Data Classification with Revised Kernel Partial Least Squares Algorithm
One important feature of the gene expression data is that the number of genes M far exceeds the number of samples N. Standard statistical methods do not work well when N < M. D...
ZhenQiu Liu, Dechang Chen