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

By Richard Khoury, Christopher Drummond

ISBN-10: 3319341103

ISBN-13: 9783319341101

ISBN-10: 3319341111

ISBN-13: 9783319341118

This e-book 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 topics: activities and behaviours, audio and visible reputation, normal language processing, reasoning and studying, streams and dispensed computing.

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Extra 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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Appl. Soft Comput. 11(2), 1565–1573 (2011) 5. : A practical guide to support vector classification. National Taiwan University (2003). pdf 6. : Keystroke dynamics as a biometric for authentication. Future Gener. Comput. Syst. 16(4), 351–359 (2000) 7. : A systematic review on keystroke dynamics. J. Braz. Comput. Soc. 19(4), 573–587 (2013) 8. : Computer user verification using login string keystroke dynamics. IEEE Trans. Syst. Man Cybern. Part A: Syst. Hum. 28(2), 236–241 (1998) 9. : Novel impostors detection in keystroke dynamics by support vector machine.

Computer-access security systems using keystroke dynamics. IEEE Trans. Pattern Anal. Mach. Intell. 12(12), 1217–1222 (1990) 3. : Identity authentication based on keystroke latencies. Commun. ACM 33(2), 168–176 (1990) 4. : Biometric personal authentication using keystroke dynamics: a review. Appl. Soft Comput. 11(2), 1565–1573 (2011) 5. : A practical guide to support vector classification. National Taiwan University (2003). pdf 6. : Keystroke dynamics as a biometric for authentication. Future Gener.

Meanwhile the False Negative rate increases and the False Positive rate decreases. If γ is very large, all training vectors will end up as support vectors, which is called Overfitting and is clearly not desirable. Otherwise if γ is very small, the frontier will be too loose and Biometric Authentication by Keystroke Dynamics 27 Underfitting appears. Thus we could get a weak accuracy in the training phase, and it will also decrease the accuracy in the testing phase. On the other hand, the parameter ν ∈ [0, 1] is used to tune the upper bound on the fraction of outliers outside the decision boundaries and the lower bound on the fraction of support vectors in the training dataset [10].

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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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