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This publication constitutes the refereed lawsuits of the second one overseas convention on complex desktop 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 a hundred and one preliminary submissions. The papers are equipped in topical sections on desktop studying in Arabic textual content acceptance and assistive expertise; advice structures for cloud providers; laptop studying in watermarking/authentication and digital machines; gains extraction and category; rough/fuzzy units and purposes; fuzzy multi-criteria selection making; Web-based program and case-based reasoning building; social networks and massive info sets.
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Extra resources for Advanced Machine Learning Technologies and Applications: Second International Conference, AMLTA 2014, Cairo, Egypt, November 28-30, 2014. Proceedings
SURF em mploys Hessian-Laplace matrix detectors to find inteerest points. A feature vector (or descriptor) is then computed to describe the neighborhoood of the interest point, by means m of local-sensitivity hashing (LSH) [17,18]. SU URF relies on Euclidean distancee between the matched descriptor and the most similar oone. Distances below a certain n pre-determined threshold are chosen as good matchhes. SURF has a computationall reduction advantage over its predecessors (mainly SIIFT ) due to the use of integ gral image in the computation of the Hessian detector .
However, those works can be intended in order to compare the different SVM kernel, and choose the best learning parameters. The reported results were obtained using only one SVM methods in experiments. The choice of kernel function is generally arbitrary. 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.
An SVM is constructed for each pair of classes by training it to discriminate the two classes. The maxwins strategy is generally used to determine the class of pattern x using a majority voting. The class with maximum number of votes is assigned to pattern. 3. Directed Acyclic Graph Support Vector Machines strategy (DAGSVM): DAGSVM is a new learning architecture which is used to combine two classifiers into one. Its works as OAO method solving k (k - 1)/2 binary SVMs, in the training step. However, it uses a rooted binary DAG which has k (k - 1)/2 internal nodes and k leaves, in the testing step.