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TNN
2008
182views more  TNN 2008»
15 years 1 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...
ICIC
2005
Springer
15 years 7 months ago
Methods of Decreasing the Number of Support Vectors via k-Mean Clustering
This paper proposes two methods which take advantage of k -mean clustering algorithm to decrease the number of support vectors (SVs) for the training of support vector machine (SVM...
Xiao-Lei Xia, Michael R. Lyu, Tat-Ming Lok, Guang-...
ICPR
2010
IEEE
15 years 5 months ago
Malware Detection on Mobile Devices Using Distributed Machine Learning
This paper presents a distributed Support Vector Machine (SVM) algorithm in order to detect malicious software (malware) on a network of mobile devices. The light-weight system mo...
Ashkan Sharifi Shamili, Christian Bauckhage, Tansu...
TREC
2004
15 years 3 months ago
Experience of Using SVM for the Triage Task in TREC 2004 Genomics Track
This paper reports our knowledge-ignorant machine learning approach to the triage task in TREC2004 genomics track, which is actually a text categorization problem. We applied Supp...
Dell Zhang, Wee Sun Lee
CLEF
2011
Springer
14 years 1 months ago
Author Identification Using Semi-supervised Learning - Notebook for PAN at CLEF 2011
Author identification models fall into two major categories according to the way they handle the training texts: profile-based models produce one representation per author while in...
Ioannis Kourtis, Efstathios Stamatatos