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https://www.researchgate.net/publication/2617438_Controlling_the_Sensitivity_of_Support_Vector_Machines
Controlling the Sensitivity of Support Vector Machines. For many applications it is important to accurately distinguish false negative results from false positives. This is particularly important for medical diagnosis where the correct balance between sensitivity and specificity plays an important role in evaluating the performance of a classifier.
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.42.7895
In this paper we discuss two schemes for adjusting the sensitivity and specificity of Support Vector Machines and the description of their performance using receiver operating characteristic (ROC) curves. We then illustrate their use on real-life medical diagnostic tasks. 1 Introduction.
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.722.369
In this paper we discuss two schemes for adjusting the sensitivity and specicity of Support Vector Machines and the description of their performance using receiver operating characteristic (ROC) curves. We then illustrate their use on real-life medical diagnostic tasks. 1 Introduction.
https://research-information.bris.ac.uk/en/publications/controlling-the-sensitivity-of-support-vector-machines(dc726780-ad9d-4044-afe8-6ecd017730ef).html
The Application of Support Vector Machines to Medical decision Support: A Case Study. Research output: Chapter in Book/Report/Conference proceeding › Conference ...Cited by: 874
http://core.ac.uk/display/23920833
Controlling the Sensitivity of Support Vector Machines ... where the correct balance between sensitivity and specificity plays an important role in evaluating the performance of a classifier. In this paper we discuss two schemes for adjusting the sensitivity and specificity of Support Vector Machines and the description of their performance ...
https://www.scirp.org/reference/ReferencesPapers.aspx?ReferenceID=2370749
Veropoulos, K., Campbell, C. and Cristianini, N. (1999) Controlling the Sensitivity of Support Vector Machines. Proceedings of the International Joint Conference on Artificial Intelligence, Stockholm, 31 July-6 August 1999, 55-60.
https://pdfs.semanticscholar.org/9806/125aeb6c83d37d805ae782b79276cf518b92.pdf
Recently Support Vector Machines (SVMs) are applied successfully for classification of hyperspectral imagery because they characterize classes by a geometrical criterion, not by statistical criteria. However, accuracy and performance sensitivity of SVMs in classification of hyperspectral imagery are affected by three different factors.
http://ijain.org/index.php/IJAIN/article/view/146
Support Vector Machine (SVM) is robust and precise method in handling class imbalance problem but weak in the bias data distribution, Biased Support Vector Machine (BSVM) became popular choice to solve the problem. BSVM provide better control sensitivity yet lack accuracy compared to general SVM.Cited by: 13
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