Titlebook: Advances in Artificial Intelligence; 28th Canadian Confer Denilson Barbosa,Evangelos Milios Conference proceedings 2015 Springer Internatio

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https://doi.org/10.1007/3-211-38173-2ly adapt to changes in dynamic data with minimal computational cost. Compared with the most popular big data classification tool, LibLinear, our approach is shown to be competent at processing extreme large data, while consuming a fractional of memory and time.

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https://doi.org/10.1007/3-211-38173-2ramming solvers. To address the scalability issues raised by this new model, we propose a general column-generation approach composed of a master and slave problem, which can also be used on the original problem of patrol generation without time-windows. Finally, we discuss and propose a new two-pha

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https://doi.org/10.1007/3-211-38173-2 is also designed to use rich information about users such as group memberships, views and likes. An experimental evaluation with 11,247 Facebook user profiles shows that PGPI predicts user profiles more accurately and by accessing a smaller part of the social graph than four state-of-the-art algori

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Marcos C. S. Carreira,Richard J. Brostowicz applications in social sciences, psychology, and health sciences. The only prior effort [.] that addresses this problem assumes equal proportion of positive, negative, and neutral tweets, but a casual observation shows that such a scenario is not realistic. So in our work, we first determine the pr

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