By Richard Khoury, Christopher Drummond
This e-book constitutes the refereed complaints of the twenty ninth Canadian convention on man made Intelligence, Canadian AI 2016, held in Victoria, BC, Canada, in May/June 2016. The 12 complete papers and 27 brief papers provided have been conscientiously reviewed and chosen from ninety seven submissions. the focal point 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 disbursed computing.
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Additional resources for Advances in Artificial Intelligence: 29th Canadian Conference on Artificial Intelligence, Canadian AI 2016, Victoria, BC, Canada, May 31 - June 3, 2016. Proceedings
An autoencoder is an artiﬁcial neural network that learns to reproduce its input data with backpropagation. Autoencoder always consists of two parts, encoder and decoder. An encoder converts the input into feature representation, and a decoder converts the feature representation back into a reconstructed input. A particular approach for developing autoencoders is Restricted Boltzmann Machines . 3 Theory and Approach We focus on the problem of detecting if a longliner vessel is in the state of ﬁshing vs.
Evaluation of the performance by the Accuracy measurement. The Accuracy tends to decrease slightly with the decreasing of the number of signatures (grey line). But the number of signatures seems to have no inﬂuence on the False Positive number (green line). (Color ﬁgure online) worse recognition rates are found. So we observed a correlation with distribution characteristics, which can be used in the recognition process and can explain why the recognition rates of some IDULs are not satisﬁed. Fig.
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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