scholarly journals Penerapan Support Vector Machine (SVM) untuk Pengkategorian Penelitian

2017 ◽  
Vol 1 (1) ◽  
pp. 19-25
Author(s):  
Fithri Selva Jumeilah
Keyword(s):  
Support Vector Machine ◽  
Support Vector Machines ◽  
Input Data ◽  
Secondary Data ◽  
Support Vector ◽  
Training Process ◽  
Knowledge Model ◽  
Term Weighting ◽  
Text Data ◽  
Vector Machines

Research every college will continue to grow. Research will be stored in softcopy and hardcopy. The preparation of the research should be categorized in order to facilitate the search for people who need reference. To categorize the research, we need a method for text mining, one of them is with the implementation of Support Vector Machines (SVM). The data used to recognize the characteristics of each category then it takes secondary data which is a collection of abstracts of research. The data will be pre-processed with several stages: case folding converts all the letters into lowercase, stop words removal removal of very common words, tokenizing discard punctuation, and stemming searching for root words by removing the prefix and suffix. Further data that has undergone preprocessing will be converted into a numerical form with for the term weighting stage that is the weighting contribution of each word. From the results of term weighting then obtained data that can be used for data training and test data. The training process is done by providing input in the form of text data that is known to the class or category. Then by using the Support Vector Machines algorithm, the input data is transformed into a rule, function, or knowledge model that can be used in the prediction process. From the results of this study obtained that the categorization of research produced by SVM has been very good. This is proven by the results of the test which resulted in an accuracy of 90%.

scholarly journals Research on Parallel Support Vector Machine Based on Spark Big Data Platform

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yao Huimin
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Big Data ◽  
Support Vector Machines ◽  
Cross Validation ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Lambda Architecture ◽  
Vector Machines ◽  
Data Platform

With the development of cloud computing and distributed cluster technology, the concept of big data has been expanded and extended in terms of capacity and value, and machine learning technology has also received unprecedented attention in recent years. Traditional machine learning algorithms cannot solve the problem of effective parallelization, so a parallelization support vector machine based on Spark big data platform is proposed. Firstly, the big data platform is designed with Lambda architecture, which is divided into three layers: Batch Layer, Serving Layer, and Speed Layer. Secondly, in order to improve the training efficiency of support vector machines on large-scale data, when merging two support vector machines, the “special points” other than support vectors are considered, that is, the points where the nonsupport vectors in one subset violate the training results of the other subset, and a cross-validation merging algorithm is proposed. Then, a parallelized support vector machine based on cross-validation is proposed, and the parallelization process of the support vector machine is realized on the Spark platform. Finally, experiments on different datasets verify the effectiveness and stability of the proposed method. Experimental results show that the proposed parallelized support vector machine has outstanding performance in speed-up ratio, training time, and prediction accuracy.

Support Vector Machine Classification applied on Weaning Trials Patients

2008 ◽  
pp. 1277-1282
Author(s):  
B.F. Giraldo ◽  
A. Garde ◽  
C. Arizmendi ◽  
R. Jané ◽  
I. Diaz ◽  
...  
Keyword(s):  
Mechanical Ventilation ◽  
Support Vector Machine ◽  
Support Vector Machines ◽  
Work Of Breathing ◽  
Selection Procedure ◽  
Respiratory Pattern ◽  
Support Vector ◽  
Vector Machines ◽  
Leave One Out ◽  
Pattern Variability

The most common reason for instituting mechanical ventilation is to decrease a patient’s work of breathing. Many attempts have been made to increase the effectiveness on the evaluation of the respiratory pattern by means of respiratory signal analysis. This work suggests a method of studying the lying differences in respiratory pattern variability between patients on weaning trials. The core of the proposed method is the use of support vector machines to classify patients into two groups, taking into account 35 features of each one, previously extracted from the respiratory flow. 146 patients from mechanical ventilation were studied: Group S of 79 patients with Successful trials, and Group F of 67 patients that Failed on the attempt to maintain spontaneous breathing and had to be reconnected. Applying a feature selection procedure based on the use of the support vector machine with leave-one-out cross-validation, it was obtained 86.67% of well classified patients into the Group S and 73.34% into Group F, using only eight of the 35 features. Therefore, support vector machines can be an interesting classification method in the study of the respiratory pattern variability.

Study on Prediction of Chaotic Time Series Using Least Squares Support Vector Machines

2014 ◽  
Vol 1061-1062 ◽  
pp. 935-938
Author(s):  
Xin You Wang ◽  
Guo Fei Gao ◽  
Zhan Qu ◽  
Hai Feng Pu
Keyword(s):  
Time Series ◽  
Support Vector Machine ◽  
Support Vector Machines ◽  
Least Squares ◽  
Prediction Accuracy ◽  
Chaotic Time Series ◽  
Support Vector ◽  
Vector Machines ◽  
Online Prediction ◽  
Better Than

The predictions of chaotic time series by applying the least squares support vector machine (LS-SVM), with comparison with the traditional-SVM and-SVM, were specified. The results show that, compared with the traditional SVM, the prediction accuracy of LS-SVM is better than the traditional SVM and more suitable for time series online prediction.

Optimal Reduced-Set Vectors for Support Vector Machines with a Quadratic Kernel

2004 ◽  
Vol 16 (9) ◽  
pp. 1769-1777 ◽  
Author(s):  
Thorsten Thies ◽  
Frank Weber
Keyword(s):  
Support Vector Machine ◽  
Support Vector Machines ◽  
Discriminant Function ◽  
Explicit Solution ◽  
Computational Cost ◽  
One Dimension ◽  
Support Vector ◽  
Homogeneous Kernel ◽  
Vector Machines

To reduce computational cost, the discriminant function of a support vector machine (SVM) should be represented using as few vectors as possible. This problem has been tackled in different ways. In this article, we develop an explicit solution in the case of a general quadratic kernel k(x, x′) = (C + Dx⊺x′)2. For a given number of vectors, this solution provides the best possible approximation and can even recover the discriminant function if the number of used vectors is large enough. The key idea is to express the inhomogeneous kernel as a homogeneous kernel on a space having one dimension more than the original one and to follow the approach of Burges (1996).

scholarly journals Prediksi Konsumsi Energi Pada Bangunan Menggunakan Metode Support Vector Machice Berbasis Algoritma Genetika

2017 ◽  
Vol 2 (1) ◽  
pp. 1
Author(s):  
Wida Prima Mustika
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Feature Selection ◽  
Energy Consumption ◽  
Support Vector Machines ◽  
Energy Use ◽  
Building Energy ◽  
Support Vector ◽  
Vector Machines ◽  
Optimization Parameters

Energy consumption is a demand for the amount of energy that must supply the building at any given time. Building energy consumption has continued increased over the last few decades all over the world, and Heating, Ventilating, and Air-Conditioning (HVAC), which has a catalytic role in regulating the temperature in the room, mostly accounted for of building energy use. Models created for in this study support vector machine and support vector machine-based models of genetic algorithm to obtain the value of accuracy or error rate or the smallest error value Root Mean Square Error (RMSE) in predicting energy consumption in buildings is more accurate. After testing the two models of support vector machines and support vector machines based on the genetic algorithm is the testing results obtained by using support vector machines where RMSE value obtained was 2,613. Next was the application of genetic algorithms to the optimization parameters C and γ values obtained RMSE error of 1.825 and a genetic algorithm for feature selection error RMSE values obtained for 1,767 of the 7 predictor variables and the selection attribute or feature resulting in the election of three attributes used. After that is done the optimization parameters and the importance of the value of feature selection mistake or error of the smallest RMSE of 1.537. Thus the support vector machine algorithm based on genetic algorithm can give a solution to the problems in the prediction of energy consumption rated the smallest mistake or error.

scholarly journals Dual possibilistic regression models of support vector machines and application in power load forecasting

2020 ◽  
Vol 16 (5) ◽  
pp. 155014772092163
Author(s):  
Xianfei Yang ◽  
Xiang Yu ◽  
Hui Lu
Keyword(s):  
Support Vector Machine ◽  
Support Vector Machines ◽  
Quadratic Programming ◽  
Regression Models ◽  
Load Forecasting ◽  
Interval Data ◽  
Support Vector ◽  
Power Load ◽  
Vector Machines ◽  
Power Load Forecasting

Power load forecasting is an important guarantee of safe, stable, and economic operation of power systems. It is appropriate to use interval data to represent fuzzy information in power load forecasting. The dual possibilistic regression models approximate the observed interval data from the outside and inside directions, respectively, which can estimate the inherent uncertainty existing in the given fuzzy phenomenon well. In this article, efficient dual possibilistic regression models of support vector machines based on solving a group of quadratic programming problems are proposed. And each quadratic programming problem containing fewer optimization variables makes the training speed of the proposed approach fast. Compared with other interval regression approaches based on support vector machines, such as quadratic loss support vector machine approach and two smaller quadratic programming problem support vector machine approach, the proposed approach is more efficient on several artificial datasets and power load dataset.

New Method for Construction Land Prediction Incorporating Genetic Algorithm and Support Vector Machines

2011 ◽  
Vol 383-390 ◽  
pp. 1629-1634
Author(s):  
Yi Yong Luo ◽  
Li Ting Zhang ◽  
Hao Zhang
Keyword(s):  
Genetic Algorithm ◽  
Land Use ◽  
Support Vector Machine ◽  
Support Vector Machines ◽  
Supply And Demand ◽  
Support Vector ◽  
Construction Land ◽  
New Model ◽  
Vector Machines ◽  
Land Supply And Demand

Considering the increasingly tense relationship between construction land supply and demand, we study the inherent rules and the spatial evolution in construction land use. In order to solve the problem of parameter optimization effectively, we analysis the fundamental theory of Support Vector Machine and finally accomplish the combination of genetic algorithm and support vector machine. Meanwhile we apply this model to analysis the construction land use and propose a new model, which is based on the support vector machines with genetic algorithm, for construction land evolution. Taking Guandu district in Kunming, Yunnan as a case, we find out that the new model is far superior to recent models in terms of predicting accuracy, algorithm complexity and computational efficiency. And therefore, we believe that this is highly precise, practical and efficient model for forecasting construction land use and evolution.

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