Search Results - subject:"Support Vector Machine"

Intelligent policy framework : natural resource conservation, knowledge and big data analytics

Xiao, Nina; Qu, Xianhe - In: Journal of innovation & knowledge : JIK 10 (2025) 2, pp. 1-10

study employed two algorithms: Bayesian inference and a weighted Support Vector Machine (SVM) algorithm based on the grey …

Persistent link: https://www.econbiz.de/10015331637

A novel robust optimization model for nonlinear Support Vector Machine

Maggioni, Francesca; Spinelli, Andrea - In: European journal of operational research : EJOR 322 (2025) 1, pp. 237-253

Persistent link: https://www.econbiz.de/10015410212

Identifying marriage markets

Bolletta, Ugo; Cherchye, Laurens; Demuynck, Thomas; … - 2024

Persistent link: https://www.econbiz.de/10015050255

A performance analysis of stochastic processes and machine learning algorithms in stock market prediction

Bouasabah, Mohammed - In: Economies : open access journal 12 (2024) 8, pp. 1-12

geometric Brownian motion (GBM) models, with that of machine learning algorithms, such as Random Forest, Support Vector Machine …

Persistent link: https://www.econbiz.de/10015070753

A novel fusion Support Vector Machine integrating weak and sphere models for classification challenges with massive data

Pimentel, Jonatha Sousa; Ospina, Raydonal; Ara, Anderson - In: Decision analytics journal 11 (2024), pp. 1-14

incorporating weak and sphere support vector machine to address classification challenges with massive datasets. Comparative … Vector Machine (SVM) is a powerful machine learning tool that has proven invaluable in classifying observations through …The unprecedented growth in data generation has necessitated the adoption of advanced analytical techniques. Support …

Persistent link: https://www.econbiz.de/10015101975

Financial distress prediction using artificial neural network partial least squares regression support vector machine hybrid model and logit model

Osoolian, Mohammad; Varahrami, Vida; Razavi, Hoda - In: Iranian economic review : journal of University of Tehran 28 (2024) 3, pp. 1022-1049

Persistent link: https://www.econbiz.de/10015402991

A comparative assessment of machine learning algorithms with the Least Absolute Shrinkage and Selection Operator for breast cancer detection and prediction

Hassan, Md. Mehedi; Hassan, Md. Mahedi; Yasmin, Farhana; … - In: Decision analytics journal 7 (2023), pp. 1-17

Boosting (XGB), Gradient Boosting (GB), Random Forest (RF), Multilayer Perceptron (MLP), and Support Vector Machine (SVM …

Persistent link: https://www.econbiz.de/10014497358

Is artificial intelligence really more accurate in predicting bankruptcy?

Letkovský, Stanislav; Jeňcová, Sylvia; … - In: International Journal of Financial Studies : open … 12 (2024) 1, pp. 1-19

Predicting bankruptcy within selected industries is crucial because of the potential ripple effects and unique characteristics of those industries. It serves as a risk management tool, guiding various stakeholders in making decisions. While artificial intelligence (AI) has shown high success...

Persistent link: https://www.econbiz.de/10014502270

Forecasting stock closing prices with an application to airline company data

Xu, Xu; Zhang, Yixiang; McGrory, Clare Anne; Wu, Jinran; … - In: Data science and management : DSM 6 (2023) 4, pp. 239-246

also compare our approach with ridge, tree, and support vector machine regressions, as well as neural network approaches to …

Persistent link: https://www.econbiz.de/10014518025

Prediction and analysis of customer complaints using machine learning techniques

Alarifi, Ghadah; Rahman, Mst Farjana; Hossain, Md Shamim - In: International journal of e-business research : IJEBR ; … 19 (2023) 1, pp. 1-25

Persistent link: https://www.econbiz.de/10015051113