Search Results - subject:"Support Vector Machine"

Short-term solar power prediction using a support vector machine

Zeng, Jianwu; Qiao, Wei - In: Renewable Energy 52 (2013) C, pp. 118-127

This paper proposes a least-square (LS) support vector machine (SVM)-based model for short-term solar power prediction …

Persistent link: https://www.econbiz.de/10010806128

Slope reliability analysis by updated support vector machine and Monte Carlo simulation

Li, Shaojun; Zhao, Hong-Bo; Ru, Zhongliang - In: Natural Hazards 65 (2013) 1, pp. 707-722

vector machine (SVM) and Monte Carlo simulation (MCS). MCS is a powerful tool that may be used to solve a broad range of …This paper presents a new methodology for slope reliability analysis by integrating the technologies of updated support …

Persistent link: https://www.econbiz.de/10010846384

Analysis of presence-only data via semi-supervised learning approaches

Wang, Junhui; Fang, Yixin - In: Computational Statistics & Data Analysis 59 (2013) C, pp. 134-143

Presence-only data occur in a classification, which consist of a sample of observations from the presence class and a large number of background observations with unknown presence/absence. Since absence data are generally unavailable, conventional semi-supervised learning approaches are no...

Persistent link: https://www.econbiz.de/10010595076

A MULTI-CLASS SUPPORT VECTOR MACHINE: THEORY AND MODEL

SUN, MINGHE - In: International Journal of Information Technology & … 12 (2013) 06, pp. 1175-1199

A multi-class support vector machine (M-SVM) is developed, its dual is derived, its dual is mapped to high dimensional …

Persistent link: https://www.econbiz.de/10010721369

Sparse high-dimensional fractional-norm support vector machine via DC programming

Guan, Wei; Gray, Alexander - In: Computational Statistics & Data Analysis 67 (2013) C, pp. 136-148

This paper considers a class of feature selecting support vector machines (SVMs) based on Lq-norm regularization, where q∈(0,1). The standard SVM [Vapnik, V., 1995. The Nature of Statistical Learning Theory. Springer, NY.] minimizes the hinge loss function subject to the L2-norm penalty....

Persistent link: https://www.econbiz.de/10011056518

Carbon price forecasting with a novel hybrid ARIMA and least squares support vector machines methodology

Zhu, Bangzhu; Wei, Yiming - In: Omega 41 (2013) 3, pp. 517-524

in time series forecasting, the ARIMA model cannot capture nonlinear patterns. The least squares support vector machine …

Persistent link: https://www.econbiz.de/10010870979

Small sample-oriented case-based kernel predictive modeling and its economic forecasting applications under n-splits-k-times hold-out assessment

Li, Hui; Hong, Lu-Yao; He, Jia-Xun; Xu, Xuan-Guo; Sun, Jie - In: Economic Modelling 33 (2013) C, pp. 747-761

investigation on the small sample-oriented case-based kernel predictive method (SSOCBKPM) by integrating support vector machine in … dimension a hyper-plane of support vector machine is constructed by reusing the most similar cases. Two datasets for firm … is more useful in economic forecasting than case-based reasoning and support vector machine since the proportion of …

Persistent link: https://www.econbiz.de/10010737989

Predicting Bankruptcy with Support Vector Machines

Härdle, Wolfgang; Moro, Rouslan A.; Schäfer, Dorothea - Sonderforschungsbereich 649: Ökonomisches Risiko, … - 2005

– the support vector machine (SVM) – to corporate bankruptcy analysis. An SVM is implemented for analysing such predictors …

Persistent link: https://www.econbiz.de/10005489966

Predicting bankruptcy with support vector machines

Härdle, Wolfgang (contributor); … - 2005

Persistent link: https://www.econbiz.de/10003036386

Bayesian Kernel-Based Classification for Financial Distress Detection

GESTEL, T. VAN; BAESENS, B.; SUYKENS, J. A.K.; POEL, D. … - Faculteit Economie en Bedrijfskunde, Universiteit Gent - 2004

Squares Support Vector Machine (LS-SVM) classifiers, also known as kernel Fisher discriminant analysis, are applied within the …

Persistent link: https://www.econbiz.de/10004983152