Terahertz time-domain spectroscopy combined with support vector machines and partial least squares-discriminant analysis applied for the diagnosis of cervical carcinoma

2015 ◽  
Vol 7 (6) ◽  
pp. 2333-2338 ◽  
Author(s):  
Na Qi ◽  
Zhuoyong Zhang ◽  
Yuhong Xiang ◽  
Yuping Yang ◽  
Xueai Liang ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Discriminant Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Time Domain ◽  
Terahertz Spectroscopy ◽  
Support Vector ◽  
Terahertz Time Domain Spectroscopy ◽  
Vector Machines ◽  
Effective Diagnosis

Combined with terahertz spectroscopy, partial least squares-discriminant analysis and support vector machines could be novel and effective diagnosis methods for cervical cancer.

Large scale classification of spectral signatures

2015 ◽  
Vol 82 (12) ◽  
Author(s):  
Matthias Richter ◽  
Thomas Längle ◽  
Jürgen Beyerer
Keyword(s):  
Support Vector Machines ◽  
Least Squares ◽  
Partial Least Squares ◽  
Large Scale ◽  
Expert Knowledge ◽  
Pharmaceutical Products ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Vector Machines

AbstractHyperspectral sensors are becoming cheaper, faster and more readily available. Apart from industry applications, manufacturers push to bring compact devices into the end-consumer market. This development gives rise to many interesting applications such as the identification of counterfeit pharmaceutical products or the classification of food stuffs. These applications require precise models of the underlying classes. However, building these models from expert knowledge is not feasible. In this paper, we propose to use machine learning techniques to infer a model of many classes from an annotated dataset instead. We investigate the use of three popular methods: support vector machines, random forest classifiers and partial least squares. In contrast to similar approaches using support vector machines, we restrict ourselves to the linear formulation and train the classifiers by solving the primal, instead of dual optimization problem. Our experiments on a large dataset show that the support vector machine approach is superior to random forests and partial least squares in classification accuracy as well as training time.

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