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» Co-Tracking Using Semi-Supervised Support Vector Machines
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ICIC
2005
Springer
15 years 8 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-...
134
Voted
ICMLA
2010
15 years 17 days ago
Using Randomised Vectors in Transcription Factor Binding Site Predictions
Finding the location of binding sites in DNA is a difficult problem. Although the location of some binding sites have been experimentally identified, other parts of the genome may ...
Faisal Rezwan, Yi Sun, Neil Davey, Rod Adams, Alis...
128
Voted
HIS
2001
15 years 4 months ago
Use of Multi-category Proximal SVM for Data Set Reduction
In this paper we describe a method for data set reduction by effective use of Multi-category Proximal Support Vector Machine (MPSVM). By using the Linear MPSVM Formulation in an it...
S. V. N. Vishwanathan, M. Narasimha Murty
ICASSP
2010
IEEE
15 years 2 months ago
Multi-class SVM optimization using MCE training with application to topic identification
This paper presents a minimum classification error (MCE) training approach for improving the accuracy of multi-class support vector machine (SVM) classifiers. We have applied th...
Timothy J. Hazen
123
Voted
NIPS
2000
15 years 4 months ago
From Margin to Sparsity
We present an improvement of Noviko 's perceptron convergence theorem. Reinterpreting this mistakebound as a margindependent sparsity guarantee allows us to give a PAC{style ...
Thore Graepel, Ralf Herbrich, Robert C. Williamson