Richard Khoury, Christopher Drummond's Advances in Artificial Intelligence: 29th Canadian PDF

By Richard Khoury, Christopher Drummond

ISBN-10: 3319341103

ISBN-13: 9783319341101

ISBN-10: 3319341111

ISBN-13: 9783319341118

This booklet constitutes the refereed lawsuits 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 offered have been conscientiously reviewed and chosen from ninety seven submissions. the point of interest of the convention was once at the following matters: activities and behaviours, audio and visible acceptance, ordinary language processing, reasoning and studying, streams and disbursed computing.

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BoVDW: bag-of-visual-and-depth-words for gesture recognition. In: ICPR, pp. 449–452. IEEE (2012) 7. : Visual perception of biological motion and a model for its analysis. Percept. Psychophy. 14(2), 201–211 (1973) 8. : Derivative dynamic time warping. SIAM (2001) 9. : On space-time interest points. IJCV 64(2–3), 107–123 (2005) 10. : Learning realistic human actions from movies. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8. IEEE (2008) 11. : Action recognition based on a bag of 3D points.

Unstructured human activity detection from RGBD images. In: ICRA, pp. 842–849. IEEE (2012) 21. : Illusory conjunctions in the perception of objects. Cogn. Psychol. 14(1), 107–141 (1982) 22. : STOP: space-time occupancy patterns for 3D action recognition from depth map sequences. , Jacobo, J. ) CIARP 2012. LNCS, vol. 7441, pp. 252–259. Springer, Heidelberg (2012) 23. : Action recognition by dense trajectories. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3169–3176. IEEE (2011) 24.

3 C. Chang et al. Classification with SVM A classification task usually involves separating data into training and testing sets. e. e. features of the keystroke durations). A SVM (Support Vector Machine) constructs a hyperplane in high or infinite dimensional space for classification [9]. The goal of SVM is to produce a model based on the training data which predicts the target values of the test data, given only the test data attributes. It’s normally used for Binary-Class classification. , l where xi ∈ Rn and y ∈ {1, −1}l the SVM requires the solution of the following optimization problem: l 1 ξi min W T W + C W,b,ξ 2 i=1 subject to yi (W T φ(xi ) + b) ≥ 1 − ξi , ξi ≥ 0.

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Advances in Artificial Intelligence: 29th Canadian Conference on Artificial Intelligence, Canadian AI 2016, Victoria, BC, Canada, May 31 - June 3, 2016. Proceedings by Richard Khoury, Christopher Drummond


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