✦ LIBER ✦

A comparative predictive analysis of neural networks (NNs), nonlinear regression and classification and regression tree (CART) models

✍ Scribed by Muhammad A. Razi; Kuriakose Athappilly

Book ID: 108127677
Publisher: Elsevier Science
Year: 2005
Tongue: English
Weight: 332 KB
Volume: 29
Category: Article
ISSN: 0957-4174
DOI: 10.1016/j.eswa.2005.01.006

No coin nor oath required. For personal study only.

📜 SIMILAR VOLUMES

Comparative analysis of regression and a

Comparative analysis of regression and artificial neural network models for wind speed prediction

✍ Mehmet Bilgili; Besir Sahin 📂 Article 📅 2010 🏛 Springer 🌐 English ⚖ 689 KB

The Use of Risk Assessment to Predict Re

The Use of Risk Assessment to Predict Recurrent Maltreatment: A Classification and Regression Tree Analysis (CART)

✍ Eve M. Sledjeski; Lisa C. Dierker; Rebecca Brigham; Eileen Breslin 📂 Article 📅 2008 🏛 Springer US 🌐 English ⚖ 204 KB

Predicting electricity energy consumptio

Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks

✍ Geoffrey K.F. Tso; Kelvin K.W. Yau 📂 Article 📅 2007 🏛 Elsevier Science 🌐 English ⚖ 239 KB

Prediction of the compressive strength o

Prediction of the compressive strength of no-slump concrete: A comparative study of regression, neural network and ANFIS models

✍ Jafar Sobhani; Meysam Najimi; Ali Reza Pourkhorshidi; Tayebeh Parhizkar 📂 Article 📅 2010 🏛 Elsevier Science 🌐 English ⚖ 716 KB

A comparison of neural network, evidenti

A comparison of neural network, evidential reasoning and multiple regression analysis in modelling bridge risks

✍ Ying-Ming Wang; Taha M.S. Elhag 📂 Article 📅 2007 🏛 Elsevier Science 🌐 English ⚖ 237 KB

A graphical sensitivity analysis for sta

A graphical sensitivity analysis for statistical climate models: application to Indian monsoon rainfall prediction by artificial neural networks and multiple linear regression models

✍ Alex J. Cannon; Ian G. McKendry 📂 Article 📅 2002 🏛 John Wiley and Sons 🌐 English ⚖ 798 KB

## Abstract A form of sensitivity analysis is described that illustrates the effects that inputs have on outputs of statistical models. The strength and sign of relationships, the types of nonlinearity, and the presence of interactions between inputs can be diagnosed using this technique. Intended