By Nicolò Cesa-Bianchi (auth.), Abdelhamid Bouchachia (eds.)
This publication constitutes the court cases of the foreign convention on Adaptive and clever structures, ICAIS 2011, held in Klagenfurt, Austria, in September 2011.
The 36 complete papers integrated in those court cases including the abstracts of four invited talks, have been conscientiously reviewed and chosen from seventy two submissions. The contributions are geared up lower than the next topical sections: incremental studying; adaptive process structure; clever approach engineering; facts mining and trend reputation; clever brokers; and computational intelligence.
Read Online or Download Adaptive and Intelligent Systems: Second International Conference, ICAIS 2011, Klagenfurt, Austria, September 6-8, 2011. Proceedings PDF
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Extra info for Adaptive and Intelligent Systems: Second International Conference, ICAIS 2011, Klagenfurt, Austria, September 6-8, 2011. Proceedings
5GHz, 3GB Ram) the generation of the training instances needs about 10 minutes time which is more or less reasonable due to the number of spatial objects to be tested. The next step, the generation of the initial decision tree is the most time consuming process and takes more than 100 minutes. Since this step can be seen as an oﬄine process, the time plays a minor role. The initial decision tree based on the given 4,000 spatial objects, contains nearly 25,000 decision nodes and shows a depth of 80 levels from the root to the leaf nodes.
This explaination is confirmed by the last column of the table 4, On-Line Human Recognition from Video-Surveillance Table 4. Recognition results of SVM with incremental learning based on four proposed database. 29 37 Table 5. 96 in which we inverted the size of training and testing dataset for the whole set of features. These second results are close to the one of classical SVM without any class badly recognized. One idea to overcome this problem is to investigate on the support and error vectors assignation (tracing them over the time) in order to find a possibility to avoid such behaviour.
In our work, we focus on supervised learning, where the class labels are known. Three methods (PCA, Wrapper and Correlation-based feature selection) are compared, in order to choose the best features for human recognition. In a first place all the dimensions are normalized to remove scale effects between them (because variation can be hugely different between dimensions). PCA. Feature selection based on PCA aims at reducing the number of dimension without losing the main information by projecting the data on a new orthogonal basis.
Adaptive and Intelligent Systems: Second International Conference, ICAIS 2011, Klagenfurt, Austria, September 6-8, 2011. Proceedings by Nicolò Cesa-Bianchi (auth.), Abdelhamid Bouchachia (eds.)