Search Results - subject:"Support Vector Machine"

Adapting support vector optimisation algorithms to textual gender classification

Gomez, Javier; Alfaro, Cesar; Ortega, Felipe; Moguerza, … - In: Top : an official journal of the Spanish Society of … 32 (2024) 3, pp. 463-488

Persistent link: https://www.econbiz.de/10015188211

Convex support vector regression

Liao, Zhiqiang; Dai, Sheng; Kuosmanen, Timo - In: European journal of operational research : EJOR 313 (2024) 3, pp. 858-870

Persistent link: https://www.econbiz.de/10014456645

Novel comparative methodology of hybrid support vector machine with meta-heuristic algorithms to develop an integrated candlestick technical analysis model

Mahmoodi, Armin; Hashemi, Leila; Mahmoodi, Amin; … - In: Journal of capital markets studies 8 (2024) 1, pp. 67-94

heuristic algorithms: support vector machine (SVM), meta-heuristic algorithms, particle swarm optimization (PSO), imperialist …

Persistent link: https://www.econbiz.de/10015047541

Novel comparative methodology of hybrid support vector machine with meta-heuristic algorithms to develop an integrated candlestick technical analysis model

Mahmoodi, Armin; Hashemi, Leila; Mahmoodi, Amin; … - In: Journal of Capital Markets Studies (JCMS) 8 (2024) 1, pp. 67-94

heuristic algorithms: support vector machine (SVM), meta-heuristic algorithms, particle swarm optimization (PSO), imperialist …

Persistent link: https://www.econbiz.de/10015327938

Modeling the effect of population size on banking transaction channels in Nigeria : Grey box vs Support Vector Regression

Bartholomew, Desmond C.; Olewuezi, Ngozi P.; Nwaigwe, … - In: CBN journal of applied statistics 15 (2024) 1, pp. 1-31

This study investigates the effect of Nigeria's population on four selected banking transaction channels. The Nigerian projected population (2022-2027) was used as an input variable for forecasting future volumes of transactions for each channel. The results show that the Support Vector...

Persistent link: https://www.econbiz.de/10015393855

A hybrid model for forecasting realized volatility based on heterogeneous autoregressive model and support vector regression

Zhuo, Yue; Morimoto, Takayuki - In: Risks : open access journal 12 (2024) 1, pp. 1-16

In this study, we proposed two types of hybrid models based on the heterogeneous autoregressive (HAR) model and support vector regression (SVR) model to forecast realized volatility (RV). The first model is a residual-type model, where the RV is first predicted using the HAR model, and the...

Persistent link: https://www.econbiz.de/10014480965

Digital identification and pattern recognition capabilities using machine learning methods, navigation systems, and video surveillance

Marchenko, Olena; Viunenko, Oleksandr; Nechai, Ihor - In: Technology audit and production reserves 1 (2024) 2/75, pp. 6-13

Persistent link: https://www.econbiz.de/10014497309

Forecasting relative returns for S&P 500 stocks using machine learning

Htet Htet Htun; Biehl, Michael; Petkov, Nicolai - In: Financial innovation : FIN 10 (2024), pp. 1-16

multiple previous relative returns and apply the random forest (RF), support vector machine (SVM), and long short-term memory …

Persistent link: https://www.econbiz.de/10014547210

Comparison of improved relevance vector machines for streamflow predictions

Adnan, Rana Muhammad; Mostafa, Reham R.; Dai, Hong-Liang; … - In: Journal of forecasting 43 (2024) 1, pp. 159-181

Persistent link: https://www.econbiz.de/10014443193

The volatility mechanism and intelligent fusion forecast of new energy stock prices

Fan, Guo-Feng; Zhang, Ruo-Tong; Cao, Cen-Cen; Peng, Li-Ling - In: Financial innovation : FIN 10 (2024), pp. 1-37

the support vector machine model parameters are selected using random wandering and picking elites via Ant Lion Opti …

Persistent link: https://www.econbiz.de/10014535574