A NOVEL ROBUST REGRESSION APPROACH OF LIDAR SIGNAL BASED ON MODIFIED LEAST SQUARES SUPPORT VECTOR MACHINE

2005 ◽  
Vol 19 (05) ◽  
pp. 715-729 ◽  
Author(s):  
BING-YU SUN ◽  
DE-SHUANG HUANG ◽  
HAI-TAO FANG ◽  
XING-MING YANG
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Robust Regression ◽  
A Priori ◽  
Support Vector ◽  
Lidar Signal ◽  
Data Set ◽  
Novel Approach ◽  
Svm Algorithm ◽  
Effectiveness And Efficiency

Lidar is an active remote sensing instrument, but its effective range is often limited by signal-to-noise (SNR) ratio. The reason is that noises or fluctuations always strongly affect the measured results. To resolve this problem, a novel approach of using least-squares support vector machine (LS-SVM) to reconstruct the Lidar signal is proposed in this paper. LS-SVM has been proven as robust to noisy data; the Lidar signal, which is strongly corrupted by noises or fluctuations, can be thought as a function of distance. So detecting Lidar signals from high noisy regime can be regarded as a robust regression procedure which involves estimating the underlying relationship from detected signal data set. To apply the LS-SVM on Lidar signal regression, firstly the noises in Lidar signal is analyzed and then the traditional LS-SVM algorithm is modified to incorporate the a priori knowledge of the Lidar signal in the training of LS-SVM. The experimental results demonstrate the effectiveness and efficiency of our approach.

scholarly journals Multi-phase classification by a least-squares support vector machine approach in tomography images of geological samples

Solid Earth ◽  
2016 ◽  
Vol 7 (2) ◽  
pp. 481-492 ◽  
Author(s):  
Faisal Khan ◽  
Frieder Enzmann ◽  
Michael Kersten
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Image Data ◽  
Main Beam ◽  
Support Vector ◽  
Geological Samples ◽  
Data Set ◽  
Corrected Image ◽  
Phase Classification ◽  
Multi Phase

Abstract. Image processing of X-ray-computed polychromatic cone-beam micro-tomography (μXCT) data of geological samples mainly involves artefact reduction and phase segmentation. For the former, the main beam-hardening (BH) artefact is removed by applying a best-fit quadratic surface algorithm to a given image data set (reconstructed slice), which minimizes the BH offsets of the attenuation data points from that surface. A Matlab code for this approach is provided in the Appendix. The final BH-corrected image is extracted from the residual data or from the difference between the surface elevation values and the original grey-scale values. For the segmentation, we propose a novel least-squares support vector machine (LS-SVM, an algorithm for pixel-based multi-phase classification) approach. A receiver operating characteristic (ROC) analysis was performed on BH-corrected and uncorrected samples to show that BH correction is in fact an important prerequisite for accurate multi-phase classification. The combination of the two approaches was thus used to classify successfully three different more or less complex multi-phase rock core samples.

Modeling of Flexible Structures by Means of Least Square Support Vector Machine

2015 ◽  
Author(s):  
Hammam Tamimi ◽  
Dirk Söffker
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Flexible Structures ◽  
Operating Conditions ◽  
Least Square ◽  
Support Vector ◽  
Suitable Model ◽  
Support Vector Machine Algorithm ◽  
Training Time ◽  
Svm Algorithm

This paper investigates modeling of flexible structures by means of the least squares support vector machine (LS-SVM) algorithm. Modeling is the first step to obtain a suitable model-based controller for any given system. Accurate modeling of a flexible structure based on experimental data using LS-SVM algorithm requires less knowledge about the physical system. Least squares support vector machine algorithm can achieve global and unique solution when compared with other soft computing algorithms. Also, LS-SVM algorithm requires less training time. In this paper, the successful use of support vector machine algorithm to model the flexible cantilever is demonstrated. The acquired model is able to provide accurate prediction of the system output under different operating conditions. Experimental results demonstrate the efficiency and high precision of the proposed approach.

Implementation of the Support Vector Machine (SVM) Algorithm in Classifying Website Phishing

2021 ◽  
Vol 9 (4) ◽  
pp. 467
Author(s):  
Putu Agus Prawira Dharma Yuda ◽  
I Putu Gede Hendra Suputra
Keyword(s):  
Support Vector Machine ◽  
Total Population ◽  
Internet Technology ◽  
Support Vector ◽  
The Internet ◽  
Data Set ◽  
Svm Algorithm ◽  
The World ◽  
Fast Development ◽  
The Times

The development of the internet is so significant, if we look at the growth of the internet in the world, it has reached more than 4 billion and in Indonesia, there are more than 171 million users out of a total population of more than 273 million people. This is due to the very fast development of information technology and various kinds of media and functions. However, of the advances in internet technology, it did not escape the existing internet attacks. One of them is phishing. Phishing is a form of activity that threatens or traps someone with the concept of luring that person. Namely by tricking someone so that the person indirectly provides all the information the trapper needs. Phishing is included in cybercrime, where crime is rampant through computer networks. Along with the times, crime is also increasingly widespread throughout the world. So that the threats that are happening today are also via computers. With such cases, this study aims to predict phishing sites with a classification algorithm. One of them is by using the SVM (Support Vector Machine) Algorithm. This research was conducted by classifying the phishing website data set and then calculating the accuracy for each kernel. From the study, the results are SVM with Gaussian RBF has the best performance with 88.92% accuracy, and SVM with Sigmoid kernel has the worst performance with 79.33% accuracy.

scholarly journals Measurement of food colour in L*a*b* units from RGB digital image using least squares support vector machine regression

2015 ◽  
Vol 46 (4) ◽  
pp. 138 ◽  
Author(s):  
Roberto Romaniello ◽  
Alessandro Leone ◽  
Giorgio Peri
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Low Cost ◽  
Digital Camera ◽  
Support Vector ◽  
Feed Forward Neural Network ◽  
Data Set ◽  
Food Colour ◽  
Input Variables ◽  
Validation Set

The aim of this work is to evaluate the potential of least squares support vector machine (LS-SVM) regression to develop an efficient method to measure the colour of food materials in L*a*b* units by means of a computer vision systems (CVS). A laboratory CVS, based on colour digital camera (CDC), was implemented and three LS-SVM models were trained and validated, one for each output variables (L*, a*, and b*) required by this problem, using the RGB signals generated by the CDC as input variables to these models. The colour target-based approach was used to camera characterization and a standard reference target of 242 colour samples was acquired using the CVS and a colorimeter. This data set was split in two sets of equal sizes, for training and validating the LS-SVM models. An effective two-stage grid search process on the parameters space was performed in MATLAB to tune the regularization parameters γ and the kernel parameters σ<sup>2</sup> of the three LS-SVM models. A 3-8-3 multilayer feed-forward neural network (MFNN), according to the research conducted by León <em>et al.</em> (2006), was also trained in order to compare its performance with those of LS-SVM models. The LS-SVM models developed in this research have been shown better generalization capability then the MFNN, allowed to obtain high correlations between L*a*b* data acquired using the colorimeter and the corresponding data obtained by transformation of the RGB data acquired by the CVS. In particular, for the validation set, R<sup>2</sup> values equal to 0.9989, 0.9987, and 0.9994 for L*, a* and b* parameters were obtained. The root mean square error values were 0.6443, 0.3226, and 0.2702 for L*, a*, and b* respectively, and the average of colour differences ΔE<sub>ab</sub> was 0.8232±0.5033 units. Thus, LS-SVM regression seems to be a useful tool to measurement of food colour using a low cost CVS.

scholarly journals Prediksi Risiko Perjalanan Transportasi Online Dari Data Telematik Menggunakan Algoritma Support Vector Machine

2020 ◽  
Vol 6 (2) ◽  
Author(s):  
Christ Memory Sitorus ◽  
Adhi Rizal ◽  
Mohamad Jajuli
Keyword(s):  
Support Vector Machine ◽  
User Satisfaction ◽  
Internet Technology ◽  
Training Data ◽  
Support Vector ◽  
Data Set ◽  
K Value ◽  
Movement Data ◽  
Svm Algorithm ◽  
Rbf Kernel

The ride-hailing service is now booming because it has been helped by internet technology, therefore many call this service online transportation. The magnitude of the potential for growth in online transportation service users also increases the risk of user satisfaction which could have declined therefore the company is increasing in its service. Both in terms of application and services provided by partners/drivers of the company. During each trip, the online transportation application will record device movement data and send it to the server. This data set is usually called telematic data. This telematics data if processed can have enormous benefits. In this study, an analysis will be conducted to predict the risk of online transportation trips using the Support Vector Machine (SVM) algorithm based on the obtained telematic data. The data obtained is telematic data so it must be processed first using feature engineering to obtain 51 features, then trained using the SVM algorithm with RBF kernel and modified C values. Every C value that is changed will be used K-Fold cross-validation first to separate the testing data and training data. The specified k value is 5. The results for each trial obtained accuracy, Receiver Operating Characteristic (ROC) and Area Under the Curves (AUC), for the best that is at C = 100 while the worst at C = 0.001.

scholarly journals Pengaruh Algoritma ADASYN dan SMOTE terhadap Performa Support Vector Machine pada Ketidakseimbangan Dataset Airbnb

2021 ◽  
Vol 5 (1) ◽  
pp. 11-20
Author(s):  
Wahyu Hidayat ◽  
◽  
Mursyid Ardiansyah ◽  
Arief Setyanto ◽  
◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Confusion Matrix ◽  
Sampling Technique ◽  
Host Population ◽  
Support Vector ◽  
Data Sets ◽  
Test Results ◽  
Data Set ◽  
Tourist Attractions ◽  
Svm Algorithm

Traveling activities are increasingly being carried out by people in the world. Some tourist attractions are difficult to reach hotels because some tourist attractions are far from the city center, Airbnb is a platform that provides home or apartment-based rentals. In lodging offers, there are two types of hosts, namely non-super host and super host. The super-host badge is obtained if the innkeeper has a good reputation and meets the requirements. There are advantages to being a super host such as having more visibility, increased earning potential and exclusive rewards. Support Vector Machine (SVM) algorithm classification process by these criteria data. Data set is unbalanced. The super host population is smaller than the non-super host. Overcoming the imbalance, this over sampling technique is carried out using ADASYN and SMOTE. Research goal was to decide the performance of ADASYN and sampling technique, SVM algorithm. Data analyses used over sampling which aims to handle unbalanced data sets, and confusion matrix used for testing Precision, Recall, and F1-SCORE, and Accuracy. Research shows that SMOTE SVM increases the accuracy rate by 1 percent from 80% to 81%, which is influenced by the increase in the True (minority) label test results and a decrease in the False label test results (majority), the SMOTE SVM is better than ADASYN SVM, and SVM without over sampling.

scholarly journals Housing Value Forecasting Based on Machine Learning Methods

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Jingyi Mu ◽  
Fang Wu ◽  
Aihua Zhang
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Big Data ◽  
Least Squares ◽  
Optimal Solution ◽  
Support Vector ◽  
Learning Methods ◽  
Data Set ◽  
Machine Learning Methods ◽  
The Government

In the era of big data, many urgent issues to tackle in all walks of life all can be solved via big data technique. Compared with the Internet, economy, industry, and aerospace fields, the application of big data in the area of architecture is relatively few. In this paper, on the basis of the actual data, the values of Boston suburb houses are forecast by several machine learning methods. According to the predictions, the government and developers can make decisions about whether developing the real estate on corresponding regions or not. In this paper, support vector machine (SVM), least squares support vector machine (LSSVM), and partial least squares (PLS) methods are used to forecast the home values. And these algorithms are compared according to the predicted results. Experiment shows that although the data set exists serious nonlinearity, the experiment result also show SVM and LSSVM methods are superior to PLS on dealing with the problem of nonlinearity. The global optimal solution can be found and best forecasting effect can be achieved by SVM because of solving a quadratic programming problem. In this paper, the different computation efficiencies of the algorithms are compared according to the computing times of relevant algorithms.

Fault Diagnosis for Temperature Signal of Turbine Blade Based on LS-SVM

2013 ◽  
Vol 385-386 ◽  
pp. 580-584 ◽  
Author(s):  
Li Wei Chen ◽  
Chen Dong Wang
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Least Squares ◽  
Turbine Blade ◽  
Small Sample ◽  
Support Vector ◽  
Support Vector Machine Algorithm ◽  
Operation Speed ◽  
Svm Algorithm ◽  
Temperature Signal

This document discusses the support vector machine (SVM) algorithm, then discusses least squares support vector machine (LS-SVM) algorithm, at the same time, the applications of SVM in the fault diagnosis of temperature signal of turbine blade being discussed, the least squares support vector machine algorithm being used in the research of fault diagnosis, being compared with LVQ neural network, experiments result show the operation speed of the least squares support vector machine algorithm is fast, its generalization ability is stronger, SVM can solve small sample learning problems as well as no-linear, high dimension and local minimization problems in the fault diagnosis of temperature signal of turbine blade.

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