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Knerr et al. (1990) present an incrementally constructed classification network, the single layer-learning network, designed to overcome the problem that the architecture of an MLP must be specified in advance (see also Knerr et al. (1992) for an application to handwriting recognition). This network results in a similar hiddewlayer to the task-based MLP. For the singlelayer network, each unit in a layer starts with random weights but, unlike a standard MLP, each unit responds only to exemplars from two classes. Hence, after training, the network should have positioned a separating hyperplane between each pair of classes. A difference between the singlelayer architecture and the task-based network is that the singleelayer learning network first of all tries to separate each class from all the other classes combined. Thus, if there are classes, then units are used to attempt the separation. If are successful, the other Q - are discarded and aii allbpairs separation is attempted for the Q remaining classes. This may lead to a smaller hidden layer than the task-based network for some problems. An example o f t h e decision surfaces arrived a t by a singlelayer learning network is illustrated in Figure 6.1, which shows a classification problem involving five classes excel ean barcode font Zint Barcode Generator | heise Download
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Solution: There's a 3 of 9 barcode font that can be used in Excel that is public ... MWSNap ( http://www.mirekw.com/winfreeware/mwsnap.html) is free and works ... in two dimensions. Class 1 is separable from all of the others combined and so would be isolated in the first stage of the algorithm. Pair-wise separation would then be attempted on classes {2,3,4,5}, needing 6 units. With the exception of classes 4 and 5, these classes would be found to be linearly separable. Below the unit assigned to separate classes 4 and 5, a sub-network would be implemented. An inspection of this diagram shows that three of the units are redundant from the point of view of positioning a separating hyperplane between each pair of classes, and so this singlelayer network has an excess number of hidden-layer units. 9.68 9.02 7.98 7.02 7.26 7.65 7.56 7.67 6.95 6.94 7.77 7.41 6.90 6.35 5.71 5.68 6.41 barcode in excel free Using the Barcode Font in Microsoft Excel (Spreadsheet)
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