Search Results - subject:"Support Vector Machine"

Data-driven risk-averse newsvendor problems : developing the CVaR criteria and support vector machines

Chen, Zhen-Yu - In: International journal of production research 62 (2024) 4, pp. 1221-1238

Persistent link: https://www.econbiz.de/10014458529

A novel method based on knowledge adoption model and non-kernel SVM for predicting the helpfulness of online reviews

Luo, Jian; Zhang, Yan; Gao, Yuanyuan; Zhang, Jing - In: Journal of the Operational Research Society 75 (2024) 6, pp. 1205-1222

Persistent link: https://www.econbiz.de/10014555893

Data-driven strategies for sustainable employee retention : leveraging machine learning to predict and prevent attrition

Krishna, Shobhanam; Borah, Debajyoti - In: South Asian journal of management : SAJM 30 (2024) 5, pp. 11-38

Persistent link: https://www.econbiz.de/10014583713

A comparative analysis of machine learning algorithms for image classification : evaluating performance

Kapse, Manohar; Elangovan, N.; Lalkiya, M.; Deshpande, … - In: Data-Driven Decision Making, (pp. 59-75). 2024

Persistent link: https://www.econbiz.de/10014633436

Short-term forecasting ability of hybrid models for BRIC currencies

Sreeram, Latha; Sayed, Samie Ahmed - In: Global business review 25 (2024) 3, pp. 585-605

Persistent link: https://www.econbiz.de/10014634305

A distributionally robust chance-constrained kernel-free quadratic surface support vector machine

Lin, Fengming; Fang, Shu-Cherng; Fang, Xiaolei; Gao, Zheming - In: European journal of operational research : EJOR 316 (2024) 1, pp. 46-60

Persistent link: https://www.econbiz.de/10014566312

Application of machine learning in quantitative investment strategies on global stock markets

Grudniewicz, Jan; Ślepaczuk, Robert - 2021

Persistent link: https://www.econbiz.de/10012816704

Using polls to forecast popular vote share for US presidential elections 2016 and 2020 : an optimal forecast combination based on ensemble empirical model

Easaw, Joshy Z.; Fang, Yongmei; Heravi, Saeed M. - 2021

elections; 2016 and 2020 US. Three models, Support Vector Machine (SVM), Neural Network (NN) and ARIMA models are then used to …

Persistent link: https://www.econbiz.de/10013177191

Measuring technical efficiency for multi-input multi-output production processes through OneClass Support Vector Machines : a finite-sample study

Moragues, Raul; Aparicio, Juan; Esteve, Miriam - In: Operational research : an international journal 23 (2023) 3, pp. 1-33

Persistent link: https://www.econbiz.de/10014364592

An unsupervised learning-based generalization of Data Envelopment Analysis

Moragues, Raul; Aparicio, Juan; Esteve, Miriam - In: Operations research perspectives 11 (2023), pp. 1-14

In this paper, we introduce an unsupervised machine learning method for production frontier estimation. This new approach satisfies fundamental properties of microeconomics, such as convexity and free disposability (shape constraints). The new method generalizes Data Envelopment Analysis (DEA)...

Persistent link: https://www.econbiz.de/10014448520