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This publication constitutes the refereed complaints of the second one overseas convention on complex laptop studying applied sciences and purposes, AMLTA 2014, held in Cairo, Egypt, in November 2014. The forty nine complete papers provided have been rigorously reviewed and chosen from one zero one preliminary submissions. The papers are prepared in topical sections on computer studying in Arabic textual content attractiveness and assistive expertise; suggestion platforms for cloud companies; computing device studying in watermarking/authentication and digital machines; beneficial properties extraction and class; rough/fuzzy units and functions; fuzzy multi-criteria determination making; Web-based software 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
Arabic sign language recognition an imagebased approach. Applied Soft Computing 9(3), 990–999 (2009) 4. : Recognition of Arabic sign language alphabet using polynomial classiﬁer. EURSIP Journal on Applied Signal Processing 13, 2136–2145 (2005) 5. : Appearance-based gesture recognition. Diploma thesis, RWTH Aachen University, Aachen, Germany (2005) 6. : Rough computing: Theories, technologies and applications. IGI Publishing Hershe (2008) 7. : Colour based hand and ﬁnger detection technology for user interaction.
LNCS, vol. 7664, pp. 85–91. Springer, Heidelberg (2012) 14. : A KNN-SVM hybrid model for cursive handwriting recognition. In: Proceedings of the International Conference on Neural Networks (IJCNN), Brazil, pp. 1–8 (2012) 15. : Rough computing: Theories, technologies and applications. lb Abstract. Smart phone technology and mobile applications have become an indispensable part of our daily life. The primary use however, is targeted towards social media and photography. While some camera-based approaches provided partial solutions for the visually impaired, they still constitute a cumbersome process for the user.
Hard Margin The hyper plane WTX+b= 0 represents a separating hyper plane of two classes, and the distance between hyper plane and nearest example is called margin. The region located between; WTX+b= +1 and WTX+b= -1 is called region of generalization. The maximization of this region is the objective of training phase. It consists to search hyper plane that maximizes margin. This hyper plane is called "Optimal Separating Hyper plane". The determination of this hyper plane passes through the determination of minimum Euclidean distance between hyper plane and nearest example of two classes called margin.