By Ali Ghorbani (auth.), Ning Zhong, Vic Callaghan, Ali A. Ghorbani, Bin Hu (eds.)
This booklet constitutes the refereed complaints of the seventh overseas convention on energetic Media know-how, AMT 2011, held in Lanzhou, China, in September 2011. The 30 revised complete papers and six keynote talks have been rigorously reviewed and chosen for inclusion within the booklet. they're grouped in topcial sections on info mining and development research in lively media; energetic human-Web interplay and social media; lively internet intelligence purposes; energetic multi-agent and community platforms; in addition to know-how intelligence.
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Extra resources for Active Media Technology: 7th International Conference, AMT 2011, Lanzhou, China, September 7-9, 2011. Proceedings
Various methods to set the strengths of the connections exist. One way is to set the weights explicitly, using a priori knowledge. Another way is to 'train' the ANN by feeding it teaching patterns and letting it change its weights according to some learning rule. For example, the weights are updated according to the gradient of the error function. For further study the reader must refer to an ANN book such as Haykin's book on theory of ANN . Fig. 1. An exemplary raw data 3 Decision Tree Learning DT as a machine learning tool uses a tree-like graph or model to operate deciding on a specific goal.
J. Am. Soc. Inform. Sci. : Search. 2020 visions. : Relational peculiarity-oriented mining. Data Min. Knowl. Disc. : Sense and the single neuron: Probing the physiology of perception. Annu. Rev. : Brain Connectivity: A new Journal emerges. : Spikes: Exploring the Neural Code. : Molecular networks as sensors and drivers of common human diseases. : Analysis of Autism Prevalence and Neurotoxins Using Combinatorial Fusion and Association Rule Mining. In: BIBE, pp. C. ): Combining Artificial Neural Nets: Ensemble and Modular MultiNet Systems.
It means of these binary ensemble can take a decision with weighed sum algorithm illustrated in . So we can combine their results according to weighs computed by the equation 1. wi , j = log( p i, j 1 − pi, j ) (1) where wi,j is the accuracy of j-th classifier in the i-th binary ensemble. It is proved that the weights obtained according to the equation 1 are optimal weights in theory. Now the two outputs of each PWCi are computed as equation 2. 34 H. Parvin, B. Minaei, and H. Alizadeh train Training Dataset Confusion Matrix Selection of EPP Error-Prone Pairclasses Multiclass Classifier Validation Dataset Tes b% selection st PWC1,1 on 1 EPP Data Bag 1 .