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This e-book constitutes the refereed complaints of the second one foreign convention on complicated computing device 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 prepared in topical sections on desktop studying in Arabic textual content popularity and assistive know-how; advice platforms for cloud providers; laptop studying in watermarking/authentication and digital machines; positive aspects extraction and category; rough/fuzzy units and functions; fuzzy multi-criteria selection making; Web-based software and case-based reasoning building; social networks and large info sets.
Read or Download Advanced Machine Learning Technologies and Applications: Second International Conference, AMLTA 2014, Cairo, Egypt, November 28-30, 2014. Proceedings PDF
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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
One-Against-one strategy (OAO): The OAO method is introduced as pairwise SVM. 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.
ISee employs ORB as the underlying object recognitio on algorithm due to its computational efficiency on mobbile platforms. The implementation results of iSee were promising and constituted a basis for more advanced developments. References 1. : Smartphone Ownership. aspx (accessed June 2014) Smartphone-Ownersh 2. gov/ 3. : LookTel-- A comprehensive platform for computer aided visual assistaance. Paper presented at the IE EEE Conference on Computer Vision and Pattern Recogniition (2010) 4. com/ios/siri/ 5.
R Rotated BREIF (rBRIEF) was proposed to ovvercome this shortcoming. Fiig. 2 depicts a performance comparison between BRIIEF, SURF, SIFT and rBRIEF. 3 ms per frame, while SIFT and SU URF require 5228 and 217 ms, reespectively. Fig. 2. Matching performancce of SIFT, SURF, BRIEF with FAST, and ROB (oFAST T + rBRIEF) unser synthetic rotations  Due to the restrictive plaatform, on which iSee runs, ORB is chosen as the undeerlying object detection algorith hm. The algorithm has 2 inputs: a training object proviided by the application (or can be b uploaded by the user) and the scene which is the im mage captured by the user using the tap described before.