Andere Kunden interessierten sich auch für
![The SADC and Regional Integration in Southern Africa: a Diagnosis]( "The SADC and Regional Integration in Southern Africa: a Diagnosis")
The SADC and Regional Integration in Southern Africa: a Diagnosis19,99 €![Efficient Issues in Vibration and Nanofluid]( "Efficient Issues in Vibration and Nanofluid")
Efficient Issues in Vibration and Nanofluid23,99 €![The Finite Element Method]( "The Finite Element Method")
The Finite Element Method155,99 €![Computational Analysis of Blood Flow via Mitral Valve and Aortic Valve]( "Computational Analysis of Blood Flow via Mitral Valve and Aortic Valve")
Computational Analysis of Blood Flow via Mitral Valve and Aortic Valve38,99 €![Blood Flow into the Main Artery via Mitral Valve]( "Blood Flow into the Main Artery via Mitral Valve")
Blood Flow into the Main Artery via Mitral Valve32,99 €![Ubiquitination PTM Sites Prediction via Random Forest Algorithm]( "Ubiquitination PTM Sites Prediction via Random Forest Algorithm")
Ubiquitination PTM Sites Prediction via Random Forest Algorithm36,99 €![Cross Diffusion Systems]( "Cross Diffusion Systems")
Cross Diffusion Systems159,95 €
Due to the rapid evolution of Web technologies and the failure of Web standards to uniformize every single technology evolution, Web developers are faced with the challenge of ensuring that their applications are correctly rendered across a broad range of browsers and platforms. To detect cross-browser incompatibilities, developers often resort to visually checking that each document produced by their application is consistently rendered across all relevant browsers. This manual testing approach is time consuming and error-prone. Existing cross-browser compatibility testing tools speed up this process. However, existing tools in this space suffer from over-sensitivity. Reducing the number of false positives produced by these testing tools is challenging, since defining criteria for classifying a difference as an incompatibility is to some extent subjective. This work presents a machine learning approach to improve the accuracy of two techniques for cross-browser compatibility testing - one based on image analysis and one based on DOM analysis. Two classification algorithms were used, namely classification trees and neural networks.