scholarly journals Automated classification of bacterial particles in flow by multiangle scatter measurement and support vector machine classifier

2008 ◽  
Vol 73A (4) ◽  
pp. 369-379 ◽  
Author(s):  
Bartek Rajwa ◽  
Murugesan Venkatapathi ◽  
Kathy Ragheb ◽  
Padmapriya P. Banada ◽  
E. Daniel Hirleman ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Support Vector Machine Classifier ◽  
Support Vector ◽  
Automated Classification

Fully automated classification of HARDI in vivo data using a support vector machine

NeuroImage ◽  
2009 ◽  
Vol 46 (3) ◽  
pp. 642-651 ◽  
Author(s):  
S. Schnell ◽  
D. Saur ◽  
B.W. Kreher ◽  
J. Hennig ◽  
H. Burkhardt ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Automated Classification

MULTI-CLUSTER SUPPORT VECTOR MACHINE CLASSIFIER FOR THE CLASSIFICATION OF SUSPICIOUS AREAS IN DIGITAL MAMMOGRAMS

2011 ◽  
Vol 10 (04) ◽  
pp. 481-494 ◽  
Author(s):  
PETER MC LEOD ◽  
BRIJESH VERMA
Keyword(s):  
Support Vector Machine ◽  
Support Vector Machine Classifier ◽  
Screening Mammography ◽  
Support Vector ◽  
Svm Classifier ◽  
Anova Analysis ◽  
Novel Technique ◽  
Digital Database ◽  
Benchmark Database

This paper presents a novel technique for the classification of suspicious areas in digital mammograms. The proposed technique is based on clustering of input data into numerous clusters and amalgamating them with a Support Vector Machine (SVM) classifier. The technique is called multi-cluster support vector machine (MCSVM) and is designed to provide a fast converging technique with good generalization abilities leading to an improved classification as a benign or malignant class. The proposed MCSVM technique has been evaluated on data from the Digital Database of Screening Mammography (DDSM) benchmark database. The experimental results showed that the proposed MCSVM classifier achieves better results than standard SVM. A paired t-test and Anova analysis showed that the results are statistically significant.

A MODIFIED LEAST SQUARES SUPPORT VECTOR MACHINE CLASSIFIER WITH APPLICATION TO CREDIT RISK ANALYSIS

2009 ◽  
Vol 08 (04) ◽  
pp. 697-710 ◽  
Author(s):  
LEAN YU ◽  
SHOUYANG WANG ◽  
JIE CAO
Keyword(s):  
Support Vector Machine ◽  
Risk Analysis ◽  
Credit Risk ◽  
Least Squares ◽  
Support Vector Machine Classifier ◽  
Support Vector ◽  
Original Form ◽  
Simple Modification ◽  
Credit Risk Analysis

In this paper, a modified least squares support vector machine classifier, called the C-variable least squares support vector machine (C-VLSSVM) classifier, is proposed for credit risk analysis. The main idea of the proposed classifier is based on the prior knowledge that different classes may have different importance for modeling and more weight should be given to classes having more importance. The C-VLSSVM classifier can be obtained by a simple modification of the regularization parameter, based on the least squares support vector machine (LSSVM) classifier, whereby more weight is given to errors in classification of important classes, than to errors in classification of unimportant classes, while keeping the regularized terms in their original form. For illustration purpose, two real-world credit data sets are used to verify the effectiveness of the C-VLSSVM classifier. Experimental results obtained reveal that the proposed C-VLSSVM classifier can produce promising classification results in credit risk analysis, relative to other classifiers listed in this study.

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