✦ LIBER ✦

Support vector regression to predict asphalt mix performance

✍ Scribed by Maher Maalouf; Naji Khoury; Theodore B. Trafalis

Publisher: John Wiley and Sons
Year: 2008
Tongue: English
Weight: 145 KB
Volume: 32
Category: Article
ISSN: 0363-9061
DOI: 10.1002/nag.718

No coin nor oath required. For personal study only.

✦ Synopsis

Abstract

Material properties are essential in the design and evaluation of pavements. In this paper, the potential of support vector regression (SVR) algorithm is explored to predict the resilient modulus (M~R~), which is an essential property in designing and evaluating pavement materials, particularly hot mix asphalt typically used in Oklahoma. SVR is a statistical learning algorithm that is applied to regression problems; in our study, SVR was shown to be superior to the least squares (LS). Compared with the widely used LS method, the results of this study show that SVR significantly reduces the mean‐squared error and improves the correlation coefficient. Copyright © 2008 John Wiley & Sons, Ltd.

📜 SIMILAR VOLUMES

Support vector regression to predict the

Support vector regression to predict the performance of stabilized aggregate bases subject to wet–dry cycles

✍ Maher Maalouf; Naji Khoury; Joakim G. Laguros; Hillel Kumin 📂 Article 📅 2011 🏛 John Wiley and Sons 🌐 English ⚖ 995 KB

SVMtm: Support vector machines to predic

SVMtm: Support vector machines to predict transmembrane segments

✍ Zheng Yuan; John S. Mattick; Rohan D. Teasdale 📂 Article 📅 2004 🏛 John Wiley and Sons 🌐 English ⚖ 81 KB

A new method has been developed for prediction of transmembrane helices using support vector machines. Different coding schemes of protein sequences were explored, and their performances were assessed by crossvalidation tests. The best performance method can predict the transmembrane helices with se

Predictive Performance of the Methods of

Predictive Performance of the Methods of Restricted and Mixed Regression Estimators

✍ H. Toutenburg; Shalabh 📂 Article 📅 1996 🏛 John Wiley and Sons 🌐 English ⚖ 416 KB

Potential of support vector regression f

Potential of support vector regression for prediction of monthly streamflow using endogenous property

✍ Rajib Maity; Parag P. Bhagwat; Ashish Bhatnagar 📂 Article 📅 2010 🏛 John Wiley and Sons 🌐 English ⚖ 215 KB

## Abstract In the recent past, a variety of statistical and other modelling approaches have been developed to capture the properties of hydrological time series for their reliable prediction. However, the extent of complexity hinders the applicability of such traditional models in many cases. Kern

Structure–Activity Relationships in Chro

Structure–Activity Relationships in Chromatography: Retention Prediction of Oligonucleotides with Support Vector Regression

✍ Oliver Kohlbacher; Sascha Quinten; Marc Sturm; Bettina M. Mayr; Christian G. Hub 📂 Article 📅 2006 🏛 John Wiley and Sons 🌐 English ⚖ 119 KB 👁 2 views

Using support vector machine to predict

Using support vector machine to predict β- and γ-turns in proteins

✍ Xiuzhen Hu; Qianzhong Li 📂 Article 📅 2008 🏛 John Wiley and Sons 🌐 English ⚖ 159 KB

## Abstract By using the composite vector with increment of diversity, position conservation scoring function, and predictive secondary structures to express the information of sequence, a support vector machine (SVM) algorithm for predicting β‐ and γ‐turns in the proteins is proposed. The 426 and