By Dekang Lin (auth.), Atefeh Farzindar, Vlado Kešelj (eds.)
This booklet constitutes the refereed court cases of the twenty third convention on man made Intelligence, Canadian AI 2010, held in Ottawa, Canada, in May/June 2010. The 22 revised complete papers provided including 26 revised brief papers, 12 papers from the graduate pupil symposium and the abstracts of three keynote displays have been rigorously reviewed and chosen from ninety submissions. The papers are equipped in topical sections on textual content category; textual content summarization and IR; reasoning and e-commerce; probabilistic laptop studying; neural networks and swarm optimization; desktop studying and knowledge mining; ordinary language processing; textual content analytics; reasoning and making plans; e-commerce; semantic net; computer studying; and knowledge mining.
Read Online or Download Advances in Artificial Intelligence: 23rd Canadian Conference on Artificial Intelligence, Canadian AI 2010, Ottawa, Canada, May 31 – June 2, 2010. Proceedings PDF
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Additional resources for Advances in Artificial Intelligence: 23rd Canadian Conference on Artificial Intelligence, Canadian AI 2010, Ottawa, Canada, May 31 – June 2, 2010. Proceedings
For the first level we needed to use fast algorithms which could work with a large number of attributes in acceptable time). After determining which algorithms satisfy these requirements, we chose the one that achieved the highest level of performance among the varieties of simple and combined complex methods available in Weka. This process for classifier selection was applied for the other levels as well. The classifiers discussed in this paper provided the highest discriminative power, compared to the other classifiers that we tried.
The third level results, using 10-fold cross-validation are in row 5 of the Tables 1 and 2. This result shows that performing the last level improves the accuracy by a valuable extra 6%. Table 2. V. V. V. 6% 100% 6 First level Classification As with the previous levels, we tried to verify the stability of the achieved results, so we applied the method on a test set with size of 10% of the data, and obtained the results shown in row 6 of Tables 1 and 2. When we considered the above results and the results of other numerous experiments that we run, we clearly observed that the stability of the system after each level rose, and at the last level, the results on cross-validation and on the test set were quite similar.
10) (λPN − λBN ) + (λBP − λPP ) Similarly, the second condition of rule (P) can be expressed as: R(aP |x) ≤ R(aN |x) ⇐⇒ P r(C|x) ≥ (λPN (λPN − λNN ) . − λNN ) + (λNP − λPP ) (11) The ﬁrst condition of rule (B) is the converse of the ﬁrst condition of rule (P). It follows, R(aB |x) ≤ R(aP |x) ⇐⇒ P r(C|x) ≤ (λPN − λBN ) . (λPN − λBN ) + (λBP − λPP ) (12) For the second condition of rule (B), we have: R(aB |x) ≤ R(aN |x) ⇐⇒ P r(C|x) ≥ (λBN − λNN ) . (13) (λBN − λNN ) + (λNP − λBP ) The ﬁrst condition of rule (N) is the converse of the second condition of rule (P) and the second condition of rule (N) is the converse of the second condition of rule (B).