By Richard Khoury, Christopher Drummond

This ebook constitutes the refereed complaints of the twenty ninth Canadian convention on synthetic Intelligence, Canadian AI 2016, held in Victoria, BC, Canada, in May/June 2016. The 12 complete papers and 27 brief papers awarded have been rigorously reviewed and chosen from ninety seven submissions. the point of interest of the convention used to be at the following matters: activities and behaviours, audio and visible popularity, traditional language processing, reasoning and studying, streams and dispensed computing.

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Additional info for Advances in Artificial Intelligence: 29th Canadian Conference on Artificial Intelligence, Canadian AI 2016, Victoria, BC, Canada, May 31 - June 3, 2016. Proceedings

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For example, it was adopted for distinguishing imposter patterns by creating a margin that separates normal patterns from imposters by many studies in the state of the art of Keystroke Dynamics. SVM has a competitive advantage regarding performance compared to Neural Network [11]. The LibSVM is apparently a good and simple tool available for classification for Keystroke Dynamics, as an open source and widely used machine learning library. 1 Data Collection Positive Data Due to the particularity of IDUL and the difference between each IDUL, we could not reuse any existing dataset.

Action recognition using super sparse coding vector with spatio-temporal awareness. , Tuytelaars, T. ) ECCV 2014, Part II. LNCS, vol. 8690, pp. 727–741. edu Abstract. There has been a tremendous growth in new sensor technology and wireless, handheld computing devices that give rise to new opportunities for smart home applications. With advances in the technology, reduced costs of operation, and the ubiquity of WiFi and cellular networks, the reach and value of smart home technology is growing giving rise to applications of smart home systems to support independent living of the elderly.

C > 0 is the penalty parameter of the error term. Furthermore, K(xi , xj ) ≡ φ(xi )T φ(xj ) is called the kernel function. With the kernel trick, the computational efficiency has been greatly improved and the SVM has been extremely successful as a non-linear classifier. Four basic kernels often used are: linear, polynomial, radial basis function (RBF) and sigmoid [5]. Then we search for an appropriate kernel function and the best values of hyper-parameters to achieve a good separation by the hyperplane in feature space that has the largest distance to the nearest training data point of any class.

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