Support Vector Machine Models for Classification

2014 ◽  
pp. 2395-2409 ◽  
Author(s):  
Minghe Sun
Keyword(s):  
Support Vector Machine ◽  
Feature Space ◽  
Machine Learning Techniques ◽  
Inner Product ◽  
High Dimensional ◽  
Support Vector ◽  
Input Space ◽  
Feature Spaces ◽  
Primal And Dual ◽  
Multi Class Classification

As machine learning techniques, support vector machines are quadratic programming models and are recent revolutionary development for classification analysis. Primal and dual formulations of support vector machine models for both two-class and multi-class classification are discussed. The dual formulations in high dimensional feature space using inner product kernels are emphasized. Nonlinear classification function or discriminant functions in high dimensional feature spaces can be constructed through the use of inner product kernels without actually mapping the data from the input space to the high dimensional feature spaces. Furthermore, the size of the dual formulation is independent of the dimension of the input space and independent of the kernels used. Two illustrative examples, one for two-class and the other for multi-class classification, are used to demonstrate the formulations of these SVM models.

A MULTI-CLASS SUPPORT VECTOR MACHINE: THEORY AND MODEL

2013 ◽  
Vol 12 (06) ◽  
pp. 1175-1199 ◽  
Author(s):  
MINGHE SUN
Keyword(s):  
Support Vector Machine ◽  
Quadratic Programming ◽  
Programming Model ◽  
Inner Product ◽  
High Dimensional ◽  
Support Vector ◽  
Test Problems ◽  
Discriminant Functions ◽  
Input Space ◽  
Feature Spaces

A multi-class support vector machine (M-SVM) is developed, its dual is derived, its dual is mapped to high dimensional feature spaces using inner product kernels, and its performance is tested. The M-SVM is formulated as a quadratic programming model. Its dual, also a quadratic programming model, is very elegant and is easier to solve than the primal. The discriminant functions can be directly constructed from the dual solution. By using inner product kernels, the M-SVM can be built and nonlinear discriminant functions can be constructed in high dimensional feature spaces without carrying out the mappings from the input space to the feature spaces. The size of the dual, measured by the number of variables and constraints, is independent of the dimension of the input space and stays the same whether the M-SVM is built in the input space or in a feature space. Compared to other models published in the literature, this M-SVM is equally or more effective. An example is presented to demonstrate the dual formulation and solution in feature spaces. Very good results were obtained on benchmark test problems from the literature.

Financial Distress Prediction Based on Support Vector Machine with a Modified Kernel Function

2016 ◽  
Vol 25 (3) ◽  
pp. 417-429
Author(s):  
Chong Wu ◽  
Lu Wang ◽  
Zhe Shi
Keyword(s):  
Support Vector Machine ◽  
Kernel Function ◽  
Financial Distress ◽  
Classification Accuracy ◽  
Feature Space ◽  
Support Vector ◽  
Input Space ◽  
Financial Distress Prediction ◽  
Support Vectors ◽  
Distress Prediction

AbstractFor the financial distress prediction model based on support vector machine, there are no theories concerning how to choose a proper kernel function in a data-dependent way. This paper proposes a method of modified kernel function that can availably enhance classification accuracy. We apply an information-geometric method to modifying a kernel that is based on the structure of the Riemannian geometry induced in the input space by the kernel. A conformal transformation of a kernel from input space to higher-dimensional feature space enlarges volume elements locally near support vectors that are situated around the classification boundary and reduce the number of support vectors. This paper takes the Gaussian radial basis function as the internal kernel. Additionally, this paper combines the above method with the theories of standard regularization and non-dimensionalization to construct the new model. In the empirical analysis section, the paper adopts the financial data of Chinese listed companies. It uses five groups of experiments with different parameters to compare the classification accuracy. We can make the conclusion that the model of modified kernel function can effectively reduce the number of support vectors, and improve the classification accuracy.

A visual terrain classification method for mobile robots’ navigation based on convolutional neural network and support vector machine

2021 ◽  
pp. 014233122098791
Author(s):  
Wanli Wang ◽  
Botao Zhang ◽  
Kaiqi Wu ◽  
Sergey A Chepinskiy ◽  
Anton A Zhilenkov ◽  
...  
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Mobile Robots ◽  
Convolutional Neural Network ◽  
Hybrid Method ◽  
Classification Accuracy ◽  
Support Vector ◽  
High Classification Accuracy ◽  
Enhance Efficiency ◽  
Multi Class Classification

In this paper, a hybrid method based on deep learning is proposed to visually classify terrains encountered by mobile robots. Considering the limited computing resource on mobile robots and the requirement for high classification accuracy, the proposed hybrid method combines a convolutional neural network with a support vector machine to keep a high classification accuracy while improve work efficiency. The key idea is that the convolutional neural network is used to finish a multi-class classification and simultaneously the support vector machine is used to make a two-class classification. The two-class classification performed by the support vector machine is aimed at one kind of terrain that users are mostly concerned with. Results of the two classifications will be consolidated to get the final classification result. The convolutional neural network used in this method is modified for the on-board usage of mobile robots. In order to enhance efficiency, the convolutional neural network has a simple architecture. The convolutional neural network and the support vector machine are trained and tested by using RGB images of six kinds of common terrains. Experimental results demonstrate that this method can help robots classify terrains accurately and efficiently. Therefore, the proposed method has a significant potential for being applied to the on-board usage of mobile robots.

scholarly journals Review On Application of Data Mining in Life Insurance

2018 ◽  
Vol 7 (4.5) ◽  
pp. 159
Author(s):  
Vaibhav A. Hiwase ◽  
Dr. Avinash J Agrawa
Keyword(s):  
Risk Factors ◽  
Data Mining ◽  
Life Insurance ◽  
Insurance Industry ◽  
Feature Space ◽  
Machine Learning Techniques ◽  
High Dimensional ◽  
Insurance Company ◽  
Financial Loss ◽  
Life Insurance Company

The growth of life insurance has been mainly depending on the risk of insured people. These risks are unevenly distributed among the people which can be captured from different characteristics and lifestyle. These unknown distribution needs to be analyzed from        historical data and use for underwriting and policy-making in life insurance industry. Traditionally risk is calculated from selected     features known as risk factors but today it becomes important to know these risk factors in high dimensional feature space. Clustering in high dimensional feature is a challenging task mainly because of the curse of dimensionality and noisy features. Hence the use of data mining and machine learning techniques should experiment to see some interesting pattern and behaviour. This will help life insurance company to protect from financial loss to the insured person and company as well. This paper focuses on analyzing hidden correlation among features and use it for risk calculation of an individual customer.  

Network traffic analysis using Machine Learning Techniques in IoT Network

2021 ◽  
Vol 9 (4) ◽  
pp. 0-0
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Network Traffic ◽  
Traffic Analysis ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Static And Dynamic Analysis ◽  
Network Traffic Analysis ◽  
Cyber Threats ◽  
Network Topologies

Internet of things devices are not very intelligent and resource-constrained; thus, they are vulnerable to cyber threats. Cyber threats would become potentially harmful and lead to infecting the machines, disrupting the network topologies, and denying services to their legitimate users. Artificial intelligence-driven methods and advanced machine learning-based network investigation prevent the network from malicious traffics. In this research, a support vector machine learning technique was used to classify normal and abnormal traffic. Network traffic analysis has been done to detect and prevent the network from malicious traffic. Static and dynamic analysis of malware has been done. Mininet emulator was selected for network design, VMware fusion for creating a virtual environment, hosting OS was Ubuntu Linux, network topology was a tree topology. Wireshark was used to open an existing pcap file that contains network traffic. The support vector machine classifier demonstrated the best performance with 99% accuracy.

Sign in / Sign up

Export Citation Format

Share Document