An overview on twin support vector machines

Yang

et al. 2015

Entropy

Aim: To detect pathological brain conditions early is a core procedure for patients so as to have enough time for treatment. Traditional manual detection is either cumbersome, or expensive, or time-consuming. We aim to offer a system that can automatically identify pathological brain images in this paper. Method: We propose a novel image feature, viz., Fractional Fourier Entropy (FRFE), which is based on the combination of Fractional Fourier Transform (FRFT) and Shannon entropy. Afterwards, the Welch's t-test (WTT) and Mahalanobis distance (MD) were harnessed to select distinguishing features. Finally, we introduced an advanced classifier: twin support vector machine (TSVM). Results: A 10ˆK-fold stratified cross validation test showed that this proposed "FRFE + WTT + TSVM" yielded an accuracy of 100.00%, 100.00%, and 99.57% on datasets that contained 66, 160, and 255 brain images, respectively. Conclusions: The proposed "FRFE + WTT + TSVM" method is superior to 20 state-of-the-art methods.

Section: Discussionmentioning

confidence: 99%

Pathological Brain Detection by a Novel Image Feature—Fractional Fourier Entropy

Wang

Yang

et al. 2015

Entropy

“…The classification method in this study is Support Vector Machine (SVM) (Ding et al, 2011). This method has the following main features: (1) Non-linear mapping is the basic theory of SVM algorithm.…”

Section: Classification Methodmentioning

confidence: 99%

Application of Classification Algorithm of Machine Learning and Buffer Analysis in Torism Regional Planning

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

et al. 2018

ABSTRACT:Remote Sensing (RS) and Geography Information System (GIS) technologies are widely used in ecological analysis and regional planning. With the advantages of large scale monitoring, combination of point and area, multiple time-phases and repeated observation, they are suitable for monitoring and analysis of environmental information in a large range. In this study, support vector machine (SVM) classification algorithm is used to monitor the land use and land cover change (LUCC), and then to perform the ecological evaluation for Chaohu lake tourism area quantitatively. The automatic classification and the quantitative spatial-temporal analysis for the Chaohu Lake basin are realized by the analysis of multi-temporal and multispectral satellite images, DEM data and slope information data. Furthermore, the ecological buffer zone analysis is also studied to set up the buffer width for each catchment area surrounding Chaohu Lake. The results of LUCC monitoring from 1992 to 2015 has shown obvious affections by human activities. Since the construction of the Chaohu Lake basin is in the crucial stage of the rapid development of urbanization, the application of RS and GIS technique can effectively provide scientific basis for land use planning, ecological management, environmental protection and tourism resources development in the Chaohu Lake Basin.

“…SVM is a data mining approach on the basis of statistiacal learning theory, which can solve regression problems(time series analysis) and pattern recognition problems(classification and discrimination) [14] . In other words, SVM is a process of machine learning, which looks for a classification hyperplane in high dimensional space to separate date points of different categories, and maximize the largest distance between points of different categories [15] .…”

Section: Dust Particles Identification Algorithmmentioning

confidence: 99%

Research on Identification of Dust Particles on COF

MATEC Web of Conferences

Gao

2016

Abstract. Chip On Film(COF) is the key component of electronic products, and is different from Printed Circuit Board(PCB). The properties of high flexibility, thin thickness, lightweight and high wiring density make it difficult to inspect COF, especially dust particles interference. Dust particles are similar to defects, and it is hard to identify dust particles from defects, so dust particles interference of quality test is the difficulty of automatic surface defect detection. In this paper, a new method to identify dust particles is discussed from abnormal area called junction points detection and machine learning method Support Vector Machine(SVM) according to the characteristics of dust particles. As a result, a 94.8% correct rate of dust particles images identification has been achieved with the method.