Predicting prostate tumour location from multiparametric MRI using Gaussian kernel support vector machines: a preliminary study

2017 ◽  
Vol 40 (1) ◽  
pp. 39-49 ◽  
Author(s):  
Yu Sun ◽  
Hayley Reynolds ◽  
Darren Wraith ◽  
Scott Williams ◽  
Mary E. Finnegan ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Multiparametric Mri ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Prostate Tumour ◽  
Vector Machines ◽  
Tumour Location ◽  
Preliminary Study ◽  
Kernel Support Vector Machines

Support Vector Machine for Recognition of White Blood Cells of Leukaemia

2011 ◽  
pp. 93-122 ◽  
Author(s):  
Stanislaw Osowski ◽  
Tomasz Markiewicz
Keyword(s):  
Support Vector Machines ◽  
Blood Cells ◽  
White Blood Cells ◽  
Myelogenous Leukaemia ◽  
Image Feature ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Vector Machines ◽  
Kernel Support Vector Machines ◽  
The Individual

This chapter presents an automatic system for white blood cell recognition in myelogenous leukaemia on the basis of the image of a bone-marrow smear. It addresses the following fundamental problems of this task: the extraction of the individual cell image of the smear, generation of different features of the cell, selection of the best features, and final recognition using an efficient classifier network based on support vector machines. The chapter proposes the complete system solving all these problems, beginning from cell extraction using the watershed algorithm; the generation of different features based on texture, geometry, morphology, and the statistical description of the intensity of the image; feature selection using linear support vector machines; and finally classification by applying Gaussian kernel support vector machines. The results of numerical experiments on the recognition of up to 17 classes of blood cells of myelogenous leukaemia have shown that the proposed system is quite accurate and may find practical application in hospitals in the diagnosis of patients suffering from leukaemia.

scholarly journals Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel

2003 ◽  
Vol 15 (7) ◽  
pp. 1667-1689 ◽  
Author(s):  
S. Sathiya Keerthi ◽  
Chih-Jen Lin
Keyword(s):  
Support Vector Machines ◽  
Model Selection ◽  
Heuristic Method ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Svm Classifier ◽  
Vector Machines ◽  
Rbf Kernel ◽  
Linear Svm ◽  
Generalization Errors

Support vector machines (SVMs) with the gaussian (RBF) kernel have been popular for practical use. Model selection in this class of SVMs involves two hyper parameters: the penalty parameter C and the kernel width σ. This letter analyzes the behavior of the SVM classifier when these hyper parameters take very small or very large values. Our results help in understanding the hyperparameter space that leads to an efficient heuristic method of searching for hyperparameter values with small generalization errors. The analysis also indicates that if complete model selection using the gaussian kernel has been conducted, there is no need to consider linear SVM.

scholarly journals Evaluation of low degree polynomial kernel support vector machines for modelling Pore-water pressure responses

2016 ◽  
Vol 59 ◽  
pp. 04003
Author(s):  
Nuraddeen Muhammad Babangida ◽  
Muhammad Raza Ul Mustafa ◽  
Khamaruzaman Wan Yusuf ◽  
Mohamed Hasnain Isa ◽  
Imran Baig
Keyword(s):  
Support Vector Machines ◽  
Pore Water ◽  
Pore Water Pressure ◽  
Water Pressure ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Degree Polynomial ◽  
Low Degree ◽  
Vector Machines ◽  
Kernel Support Vector Machines
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