Classification of Ovarian Tumors Using Bayesian Least Squares Support Vector Machines

2003 ◽  
pp. 219-228 ◽  
Author(s):  
Chuan Lu ◽  
Tony Van-Gestel ◽  
Johan A. K. Suykens ◽  
Sabine Van-Huffel ◽  
Dirk Timmerman ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Least Squares ◽  
Ovarian Tumors ◽  
Support Vector ◽  
Vector Machines ◽  
Bayesian Least Squares

Preoperative prediction of malignancy of ovarian tumors using least squares support vector machines

2003 ◽  
Vol 28 (3) ◽  
pp. 281-306 ◽  
Author(s):  
C. Lu ◽  
T. Van Gestel ◽  
J.A.K. Suykens ◽  
S. Van Huffel ◽  
I. Vergote ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Least Squares ◽  
Ovarian Tumors ◽  
Support Vector ◽  
Preoperative Prediction ◽  
Vector Machines

Classification of adulterated milk with the parameterization of 2D correlation spectroscopy and least squares support vector machines

2013 ◽  
Vol 5 (21) ◽  
pp. 5949 ◽  
Author(s):  
Renjie Yang ◽  
Rong Liu ◽  
Kexin Xu ◽  
Yanrong Yang ◽  
Guimei Dong ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Least Squares ◽  
Correlation Spectroscopy ◽  
Support Vector ◽  
Vector Machines ◽  
2D Correlation Spectroscopy

Classification of persimmon fruit origin by near infrared spectrometry and least squares-support vector machines

2014 ◽  
Vol 142 ◽  
pp. 17-22 ◽  
Author(s):  
M. Khanmohammadi ◽  
F. Karami ◽  
A. Mir-Marqués ◽  
A. Bagheri Garmarudi ◽  
S. Garrigues ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Least Squares ◽  
Near Infrared ◽  
Infrared Spectrometry ◽  
Support Vector ◽  
Persimmon Fruit ◽  
Vector Machines ◽  
Near Infrared Spectrometry

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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