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

By Richard Khoury, Christopher Drummond

This ebook constitutes the refereed court cases 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 awarded have been rigorously 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, common language processing, reasoning and studying, streams and disbursed 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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Here, we use as many negative signatures as positive ones for each IDUL. In order to avoid the influence of random selection of data to the classification process, we repeat the positive data separation to further carry out the experiment for 3 times. During the training phase, we use Data 1 and RBF kernel to train the classifier model for each IDUL. The best value pair of hyper-parameters (γ, ν) is searched to find the best model. In this phase, a 5-fold Cross-Validation2 is used to relieve the Overfitting problem.

5. It points out that the Accuracy tends to decrease slightly with the decreasing of the number of signatures (from 197 to 12). However, it appears that the number of signatures has no influence on the False Positive number (green trend line at the bottom of the figure). For some signatures, though the Recall value was good for the model training (see Fig. 4), we found that the performance can be poor with low Accuracy value (Fig. 5). For instance, the IDUL 17 showed a quite high Recall value in the training phase, while the obtained Accuracy was quite low in the testing phase.

Khoury and C. ): Canadian AI 2016, LNAI 9673, pp. 15–20, 2016. 1007/978-3-319-34111-8_2 16 T. Reichherzer et al. To provide such capabilities, the technology must acquire contextual information from sensors in the home and reason without intervention or maintenance to make decisions and provide support. The technology must be extensible allowing newly available sensors to be easily integrated into the smart home while not interfering with other, existing components of the system. Furthermore, the system’s reasoning capabilities must be adaptable using machine-learning techniques to be able to change to subtle variations in the behavior patterns of the resident and new emerging needs in the home.

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