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106
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BMCBI
2005
251views more  BMCBI 2005»
14 years 11 months ago
Contextual weighting for Support Vector Machines in literature mining: an application to gene versus protein name disambiguation
Background: The ability to distinguish between genes and proteins is essential for understanding biological text. Support Vector Machines (SVMs) have been proven to be very effici...
Tapio Pahikkala, Filip Ginter, Jorma Boberg, Jouni...
TNN
2008
182views more  TNN 2008»
14 years 11 months ago
Large-Scale Maximum Margin Discriminant Analysis Using Core Vector Machines
Abstract--Large-margin methods, such as support vector machines (SVMs), have been very successful in classification problems. Recently, maximum margin discriminant analysis (MMDA) ...
Ivor Wai-Hung Tsang, András Kocsor, James T...
81
Voted
DCC
2009
IEEE
16 years 7 days ago
Compressed Kernel Perceptrons
Kernel machines are a popular class of machine learning algorithms that achieve state of the art accuracies on many real-life classification problems. Kernel perceptrons are among...
Slobodan Vucetic, Vladimir Coric, Zhuang Wang
99
Voted
EACL
2006
ACL Anthology
15 years 1 months ago
Making Tree Kernels Practical for Natural Language Learning
In recent years tree kernels have been proposed for the automatic learning of natural language applications. Unfortunately, they show (a) an inherent super linear complexity and (...
Alessandro Moschitti
ISNN
2005
Springer
15 years 5 months ago
Scaling the Kernel Function to Improve Performance of the Support Vector Machine
Abstract. The present study investigates a geometrical method for optimizing the kernel function of a support vector machine. The method is an improvement of the one proposed in [4...
Peter Williams, Sheng Li, Jianfeng Feng, Si Wu