Fast kernel SVM training via support vector identification

2016 ◽  
Author(s):  
Xue Mao ◽  
Zhouyu Fu ◽  
Ou Wu ◽  
Weiming Hu
Keyword(s):  
Support Vector ◽  
Kernel Svm

scholarly journals IMPLEMENTASI ALGORITMA MULTICLASS SVM PADA OPINI PUBLIK BERBAHASA INDONESIA DI TWITTER

2020 ◽  
Vol 14 (2) ◽  
pp. 86
Author(s):  
Debby Alita ◽  
Yusra Fernando ◽  
Heni Sulistiani
Keyword(s):  
Support Vector Machine ◽  
Text Mining ◽  
Support Vector ◽  
Multiclass Svm ◽  
Kernel Svm

Klasifikasi pada proses text mining dapat dikerjakan dengan menggunakan berbagai jenis metode klasifikasi yang salah satunya yaitu metode SVM. SVM merupakan singkatan dari Support Vector Machine, SVM bekerja dengan membagi dua kelompok kelas data menggunakan fungsi linear dalam sebuah ruang fitur berdimensi tinggi dengan proses menemukan garis pemisah (hyperplane) terbaik sehingga dapat menemukan ukuran margin yang maksimal antara ruang input dengan ruang ciri menggunakan kaidah kernel. SVM telah dikembangkan dengan menggabungkan semua data yang terdiri dari beberapa kelas kedalam sebuah bentuk optimasi untuk memecahkan permasalahan yang terdapat pada penelitian ini dengan jumlah kelas yang melebihi dari dua kelas dan akan diuji dengan berbagai jenis pendekatan multiclass yaitu SVM One Against One dan One Against Rest. Data merupakan opini publik berbahasa Indonesia yang didapatkan dari twitter berjumlah 2000 dataset mengenai jaringan telekomunikasi seluler dan layanan BPJS. Hasil penelitian ini didapatkan bahwa untuk penilaian kinerja metode multiclass SVM dengan tingkat akurasi yang lebih tinggi didapatkan dari kinerja metode SVM One Against Rest dengan nilai perbedaan sebesar 0,06 untuk proses klasifikasi tiga kelas yaitu positif, negatif dan netral. Dapat disimpulkan bahwa dalam proses klasifikasi yang memiliki lebih dari dua kelas dapat dilakukan dengan menggunakan metode klasifikasi SVM melalui pendekatan SVM One Against One dan One Against Rest dengan nilai akurasi yang lebih baik.

scholarly journals Study on Evaluation Model of Chinese P2P Online Lending Platform Based on Hybrid Kernel Support Vector Machine

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Shuang Pan ◽  
Jianguo Wei ◽  
Hao Pan
Keyword(s):  
Support Vector Machine ◽  
Evaluation Model ◽  
Risk Level ◽  
Support Vector ◽  
Empirical Parameter ◽  
Kernel Support Vector Machine ◽  
Svm Model ◽  
Online Lending ◽  
Kernel Svm ◽  
Hybrid Kernel

Accurate evaluation of the risk level and operation performances of P2P online lending platforms is not only conducive to better functioning of information intermediaries but also effective protection of investors’ interests. This paper proposes a genetic algorithm (GA) improved hybrid kernel support vector machine (SVM) with an index system to construct such an evaluation model. A hybrid kernel consisting of polynomial function and radial basis function is improved, specifically kernel parameters and the weight of two kernels, by GA method with excellent global optimization and rapid convergence. Empirical testing based on cross-sectional data from Chinese P2P lending market demonstrates the superiority of the improved hybrid kernel SVM model. The classification accuracy of credit risk level and operation quality is higher than the single kernel SVM model as well as the hybrid kernel model with empirical parameter values.

Classifying ordered-disordered proteins using linear and kernel support vector machines

2016 ◽  
Vol 41 (6) ◽  
Author(s):  
Çağın Kandemir Çavaş ◽  
Selen Yildirim
Keyword(s):  
Support Vector Machines ◽  
Intrinsically Disordered Proteins ◽  
Tertiary Structure ◽  
Disordered Proteins ◽  
Support Vector ◽  
Intrinsically Disordered ◽  
Vector Machines ◽  
Kernel Support Vector Machines ◽  
Linear Svm ◽  
Kernel Svm

AbstractIntroduction:Intrinsically disordered proteins occur when the deformations happen in the tertiary structure of a protein. Disordered proteins play an important role in DNA/RNA/protein recognition, modulation of specificity/affinity of protein binding, molecular threading, activation by cleavage. The aim of the study is the identification of ordered-disordered protein which is a very challenging problem in bioinformatics.Methods:In this paper, this kind of proteins is classified by using linear and kernel (nonlinear) support vector machines (SVM).Results:Overall accuracy rate of linear SVM and kernel SVM in identifying the ordered-disordered proteins are 86.54% and 94.23%, respectively.Discussion and conclusion:Since kernel SVM gives the best discriminating scheme, it can be referred that it is a very satisfying method to identify ordered-disordered structures of proteins.

Support vector machine? – not only for MRI-images

2020 ◽  
Author(s):  
V Vasilevska ◽  
K Schlaaf ◽  
H Dobrowolny ◽  
G Meyer-Lotz ◽  
HG Bernstein ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Support Vector

Helicopter main reduction planetary gear fault diagnosis method based on SVDD

2020 ◽  
Vol 64 (1-4) ◽  
pp. 137-145
Author(s):  
Yubin Xia ◽  
Dakai Liang ◽  
Guo Zheng ◽  
Jingling Wang ◽  
Jie Zeng
Keyword(s):  
Fault Diagnosis ◽  
Planetary Gear ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Energy Characteristics ◽  
Gear Fault ◽  
Channel Information ◽  
Diagnosis Method

Aiming at the irregularity of the fault characteristics of the helicopter main reducer planetary gear, a fault diagnosis method based on support vector data description (SVDD) is proposed. The working condition of the helicopter is complex and changeable, and the fault characteristics of the planetary gear also show irregularity with the change of working conditions. It is impossible to diagnose the fault by the regularity of a single fault feature; so a method of SVDD based on Gaussian kernel function is used. By connecting the energy characteristics and fault characteristics of the helicopter main reducer running state signal and performing vector quantization, the planetary gear of the helicopter main reducer is characterized, and simultaneously couple the multi-channel information, which can accurately characterize the operational state of the planetary gear’s state.

Research on behavior recognition based on feature fusion of automatic coder and recurrent neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8927-8935
Author(s):  
Bing Zheng ◽  
Dawei Yun ◽  
Yan Liang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Behavior Pattern ◽  
Rapid Development ◽  
Video Data ◽  
Support Vector ◽  
Behavior Recognition ◽  
Learning Methods ◽  
The Impact ◽  
Internet Of Things Technology

Under the impact of COVID-19, research on behavior recognition are highly needed. In this paper, we combine the algorithm of self-adaptive coder and recurrent neural network to realize the research of behavior pattern recognition. At present, most of the research of human behavior recognition is focused on the video data, which is based on the video number. At the same time, due to the complexity of video image data, it is easy to violate personal privacy. With the rapid development of Internet of things technology, it has attracted the attention of a large number of experts and scholars. Researchers have tried to use many machine learning methods, such as random forest, support vector machine and other shallow learning methods, which perform well in the laboratory environment, but there is still a long way to go from practical application. In this paper, a recursive neural network algorithm based on long and short term memory (LSTM) is proposed to realize the recognition of behavior patterns, so as to improve the accuracy of human activity behavior recognition.

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE

2019 ◽  
Vol 15 (2) ◽  
pp. 275-280
Author(s):  
Agus Setiyono ◽  
Hilman F Pardede
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Spam Detection ◽  
Support Vector Machine Algorithm ◽  
Data Mining Techniques ◽  
To Receive

It is now common for a cellphone to receive spam messages. Great number of received messages making it difficult for human to classify those messages to Spam or no Spam.  One way to overcome this problem is to use Data Mining for automatic classifications. In this paper, we investigate various data mining techniques, named Support Vector Machine, Multinomial Naïve Bayes and Decision Tree for automatic spam detection. Our experimental results show that Support Vector Machine algorithm is the best algorithm over three evaluated algorithms. Support Vector Machine achieves 98.33%, while Multinomial Naïve Bayes achieves 98.13% and Decision Tree is at 97.10 % accuracy.

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