Advanced Machine Learning Technologies and Applications: - download pdf or read online

By Aboul Ella Hassanien, Mohamed Tolba, Visit Amazon's Ahmad Taher Azar Page, search results, Learn about Author Central, Ahmad Taher Azar,

ISBN-10: 3319134604

ISBN-13: 9783319134604

ISBN-10: 3319134612

ISBN-13: 9783319134611

This e-book constitutes the refereed court cases of the second one overseas convention on complex laptop studying applied sciences and functions, AMLTA 2014, held in Cairo, Egypt, in November 2014. The forty nine complete papers offered have been conscientiously reviewed and chosen from one zero one preliminary submissions. The papers are geared up in topical sections on laptop studying in Arabic textual content acceptance and assistive expertise; advice platforms for cloud prone; desktop studying in watermarking/authentication and digital machines; positive aspects extraction and type; rough/fuzzy units and functions; fuzzy multi-criteria selection making; Web-based program and case-based reasoning development; social networks and large information sets.

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Additional info for Advanced Machine Learning Technologies and Applications: Second International Conference, AMLTA 2014, Cairo, Egypt, November 28-30, 2014. Proceedings

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This leaves a question that how these kernel function and MSVM-methods will perform when applied to Arabic scripts. Therefore, it is important to look into the discriminative power of each SVM-method and function kernel proposed in the literature before using it. The hybridization of SVM with other tools such as KNN and MLP presents an encouragement recognition rate when it’s applied to Latin character [15]. Experiments show that SVM achieves better generalization ability than MLP classifier and Naive Bayes [13].

6 . 32 In 2012, Al-Hamadi et al [11] propose a MSVM classifier for handwritten Arabic text recognition. Features are extracted based Gabor-filter. For training and testing, IESK-ar Database was used [12]. IESK-ar is an Arabic off-line handwritten database, developed in the Institute for Electronics freely available. In this work, a class for each letter shape was created. One-vs-all SVM Classifiers with RBF kernel was used. In experiments step, libsvm [8] library is used. 5436 letters were used in training of SVM classifier.

Two implementation options were available for openCV: JavaCV or the native C++ alternative. Both options can be compiled and used with the Android SDK. Our choice however was set to using the native C++ due to its faster performance and wider library functions. This performance improvement came at the expense of additional implementation complexities. First, Android NDK (Native Development Kit) had to be installed and linked with Eclipse. The NDK is a toolset that allows parts of an app to be implemented using native-code languages such as C and C++.

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Advanced Machine Learning Technologies and Applications: Second International Conference, AMLTA 2014, Cairo, Egypt, November 28-30, 2014. Proceedings by Aboul Ella Hassanien, Mohamed Tolba, Visit Amazon's Ahmad Taher Azar Page, search results, Learn about Author Central, Ahmad Taher Azar,


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