State-of-the-Art Least Square Support Vector Machine Application for Accurate Determination of Natural Gas Viscosity

2013 ◽  
Vol 53 (2) ◽  
pp. 945-958 ◽  
Author(s):  
Amir Fayazi ◽  
Milad Arabloo ◽  
Amin Shokrollahi ◽  
Mohammad Hadi Zargari ◽  
Mohammad Hossein Ghazanfari
Keyword(s):  
Support Vector Machine ◽  
Natural Gas ◽  
State Of The Art ◽  
Accurate Determination ◽  
Least Square ◽  
Support Vector ◽  
Gas Viscosity

Utilization of Classification Techniques for the Determination of Liquefaction Susceptibility of Soils

2016 ◽  
pp. 124-160 ◽  
Author(s):  
J. Jagan ◽  
Prabhakar Gundlapalli ◽  
Pijush Samui
Keyword(s):  
Support Vector Machine ◽  
Earthquake Engineering ◽  
Relevance Vector Machine ◽  
Least Square ◽  
Support Vector ◽  
Ground Acceleration ◽  
Geotechnical Earthquake Engineering ◽  
Liquefaction Susceptibility ◽  
Input Variables

The determination of liquefaction susceptibility of soil is a paramount project in geotechnical earthquake engineering. This chapter adopts Support Vector Machine (SVM), Relevance Vector Machine (RVM) and Least Square Support Vector Machine (LSSVM) for determination of liquefaction susceptibility based on Cone Penetration Test (CPT) from Chi-Chi earthquake. Input variables of SVM, RVM and LSSVM are Cone Resistance (qc) and Peak Ground Acceleration (amax/g). SVM, RVM and LSSVM have been used as classification tools. The developed SVM, RVM and LSSVM give equations for determination of liquefaction susceptibility of soil. The comparison between the developed models has been carried out. The results show that SVM, RVM and LSSVM are the robust models for determination of liquefaction susceptibility of soil.

Histogram Based Bacteria Colony Features Analysis

2014 ◽  
Vol 609-610 ◽  
pp. 1448-1452
Author(s):  
Kun Zhang ◽  
Min Rui Fei
Keyword(s):  
Support Vector Machine ◽  
Support Vector Machine Classifier ◽  
Recognition Accuracy ◽  
State Of The Art ◽  
Least Square ◽  
Support Vector ◽  
Least Square Estimation ◽  
Novel Approach ◽  
Traditional Approaches ◽  
Improved Support Vector Machine

Features analysis is an important task which can significantly affect the performance of automatic bacteria colony picking. This paper presents a novel approach for adaptive colony segmentation by classifying the detected peaks of intensity histograms of images. The relevance and importance of these features can be determined in an improved support vector machine classifier using unascertained least square estimation. Experimental results show that the proposed unascertained support vector machine (USVM) has better recognition accuracy than the other state of the art techniques, and its training process takes less time than most of the traditional approaches presented in this paper.

scholarly journals A Support Vector Machine Classifier for Recognizing Mitotic Subphases Using High-Content Screening Data

2007 ◽  
Vol 12 (4) ◽  
pp. 490-496 ◽  
Author(s):  
Charles Y. Tao ◽  
Jonathan Hoyt ◽  
Yan Feng
Keyword(s):  
Support Vector Machine ◽  
Support Vector Machine Classifier ◽  
Cross Validation ◽  
Accurate Determination ◽  
High Content Screening ◽  
Support Vector ◽  
Svm Classifier ◽  
Automated Classifier ◽  
Resolution Cell

High-content screening studies of mitotic checkpoints are important for identifying cancer targets and developing novel cancer-specific therapies. A crucial step in such a study is to determine the stage of cell cycle. Due to the overwhelming number of cells assayed in a high-content screening experiment and the multiple factors that need to be taken into consideration for accurate determination of mitotic subphases, an automated classifier is necessary. In this article, the authors describe in detail a support vector machine (SVM) classifier that they have implemented to recognize various mitotic subphases. In contrast to previous studies to recognize subcellular patterns, they used only low-resolution cell images and a few parameters that can be calculated inexpensively with off-the-shelf image-processing software. The performance of the SVM was evaluated with a cross-validation method and was shown to be comparable to that of a human expert. ( Journal of Biomolecular Screening 2007:490-496)

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