1,720,995 research outputs found
Panel data regression and support vector regression for Indonesian private external debt analysis
No full textApplication of Support Vector Regression in Indonesian Stock Price Prediction with Feature Selection Using Particle Swarm Optimisation
No full textStock investing is one of the most popular types of investments since it provides the highest return among all investment types; however, it is also associated with considerable risk. Fluctuating stock prices provide an opportunity for investors to make a high profit. We can see the movement of groups of stock prices from the stock index, which is called Jakarta Composite Index (JKSE) in Indonesia. Several studies have focused on the prediction of stock prices using machine learning, while one uses support vector regression (SVR). Therefore, this study examines the application of SVR and particle swarm optimisation (PSO) in predicting stock prices using stock historical data and several technical indicators, which are selected using PSO. Subsequently, a support vector machine (SVM) was applied to predict stock prices with the technical indicator selected by PSO as the predictor. The study found that stock price prediction using SVR and PSO shows good performances for all data, and many features and training data used by the study have relatively low error probabilities. Thereby, an accurate model was obtained to predict stock prices in Indonesia
Insolvency Prediction in Insurance Companies Using Support Vector Machines and Fuzzy Kernel C-Means
No full textComparison between Support Vector Machine and Fuzzy C-Means as Classifier for Intrusion Detection System
No full textThe Application of Multi-Class Support Vector Machines on Intrusion Detection System with the Feature Selection using Information Gain
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Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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