Search Results - subject:"Support Vector Machine"

A load predictive energy management system for supercapacitor-battery hybrid energy storage system in solar application using the Support Vector Machine

Chia, Yen Yee; Lee, Lam Hong; Shafiabady, Niusha; Isa, Dino - In: Applied Energy 137 (2015) C, pp. 588-602

This paper presents the use of a Support Vector Machine load predictive energy management system to control the energy …

Persistent link: https://www.econbiz.de/10011116136

The study and application of a novel hybrid forecasting model – A case study of wind speed forecasting in China

Wang, Jian-Zhou; Wang, Yun; Jiang, Ping - In: Applied Energy 143 (2015) C, pp. 472-488

Vector Machine (LSSVM), the parameters of which were tuned by a particle swarm optimization based on simulated annealing …) was employed to decompose the wind speed series into several series with different frequencies. A Least Square Support …

Persistent link: https://www.econbiz.de/10011208414

Seismic vulnerability assessment of urban environments in moderate-to-low seismic hazard regions using association rule learning and support vector machine methods

Riedel, Ismaël; Guéguen, Philippe; Mura, Mauro Dalla; … - In: Natural Hazards 76 (2015) 2, pp. 1111-1141

and support vector machine—we developed seismic vulnerability proxies. These were applied to the whole France using basic …

Persistent link: https://www.econbiz.de/10011241072

An assessment of multivariate and bivariate approaches in landslide susceptibility mapping: a case study of Duzkoy district

Kavzoglu, Taskin; Sahin, Emrehan Kutlug; Colkesen, Ismail - In: Natural Hazards 76 (2015) 1, pp. 471-496

Landslide susceptibility maps are valuable sources for disaster mitigation works and future investments of local authorities in unstable hazard-prone areas. However, there are limitations and uncertainties inherent in landslide susceptibility assessment. For this purpose, many methods have been...

Persistent link: https://www.econbiz.de/10011241160

Mapping of rainfall-induced landslide susceptibility in Wencheng, China, using support vector machine

Su, Cheng; Wang, Lili; Wang, Xizhi; Huang, Zhicai; … - In: Natural Hazards 76 (2015) 3, pp. 1759-1779

Province, China, using support vector machine (SVM). Seven conditioning factors of elevation, slope, aspect, distance to roads …

Persistent link: https://www.econbiz.de/10011241169

Expansion of a glacial lake, Tsho Chubda, Chamkhar Chu Basin, Hindukush Himalaya, Bhutan

Jain, Sanjay; Sinha, Rishitosh; Chaudhary, Anju; … - In: Natural Hazards 75 (2015) 2, pp. 1451-1464

performed using NDWI technique and support vector machine image classification approach and results were compared. An integrated …

Persistent link: https://www.econbiz.de/10011151448

A paratactic subjective-objective weighting methods and SVM risk assessment model applied in textile and apparel safety

Wu, Xiongying; Chen, Lihong; Pang, Shuhui; Ding, Xuemei - In: International Journal of Quality & Reliability Management 32 (2015) 5, pp. 472-485

apparel based on support vector machine (SVM) is built, and empirical analysis is also made. Findings – Quantitative and …

Persistent link: https://www.econbiz.de/10014801845

A Comparative Analysis of Support Vector Machines & Logistic Regression for Propensity Based Response Modeling

Kota, Krishna - 2015

Increasing cost of soliciting customers along with amplified efforts to improve the bottom-line amidst intense competition is driving the firms to rely on more cutting edge analytic methods by leveraging the knowledge of customer-base that is allowing the firms to engage better with customers by...

Persistent link: https://www.econbiz.de/10013021579

Support vector regression for loss given default modelling

Yao, Xiao; Crook, Jonathan N.; Andreeva, Galina - In: European journal of operational research : EJOR 240 (2015) 2, pp. 528-538

Persistent link: https://www.econbiz.de/10010487012

Advanced conjoint analysis using feature selection via support vector machines

Maldonado, Sebastián; Montoya, Ricardo; Weber, Richard - In: European journal of operational research : EJOR 241 (2015) 2, pp. 564-574

Persistent link: https://www.econbiz.de/10010487970