Peramalan Jumlah Penumpang Datang Melalui Transportasi Udara Di Sulawesi Tengah Menggunakan Support Vector Regression (SVR)

Tehran Stock Price Modeling and Forecasting Using Support Vector Regression (SVR) and Its Comparison with the Classic Model ARIMA

Soil moisture estimation by using active microwave measurements and support vector regression (SVR)

In this study the capabilities of an empirical regression approach for retrieving land surface soil moisture were investigated, by combining time series of ASCAT observations and in-situ data. The Support Vector Regression (SVR) is a novel technique that is utilized for establishing a direct relationship between remote sensing observations and the geophysical parameter of interest. ASCAT series...

Application of Hybrid Quantum Tabu Search with Support Vector Regression (SVR) for Load Forecasting

Hybridizing chaotic evolutionary algorithms with support vector regression (SVR) to improve forecasting accuracy is a hot topic in electricity load forecasting. Trapping at local optima and premature convergence are critical shortcomings of the tabu search (TS) algorithm. This paper investigates potential improvements of the TS algorithm by applying quantum computing mechanics to enhance the se...

Balanced Support Vector Regression

We propose a novel idea of regression – balancing the distances from a regression function to all examples. We created a method, called balanced support vector regression (balanced SVR) in which we incorporated this idea to support vector regression (SVR) by adding an equality constraint to the SVR optimization problem. We implemented our method for two versions of SVR: ε-insensitive support ve...

Support Vector Ordinal Regression

In this letter, we propose two new support vector approaches for ordinal regression, which optimize multiple thresholds to define parallel discriminant hyperplanes for the ordinal scales. Both approaches guarantee that the thresholds are properly ordered at the optimal solution. The size of these optimization problems is linear in the number of training samples. The sequential minimal optimizat...