This paper presents a powerful method of an automatically generated architecture of neural networks used for binary patterns recognition, which can quickly and automatically reduce synapses in a way of minimally reducing a quality of recognition and a quality of generalization. Moreover, this method computes all weights in two runs over a learning sequence, what makes this method very fast. First, the method calculates all binary features for each pattern and then weights are computed. Furthermore, there is a quality of generalization considered because it is one of the most important factors of recognition while using neural networks.
(Wydawnictwa AGH, 2009) Gadamer, Marcin; Horzyk, Adrian
Automatic text correction is an essential problem of today text processors and editors. This paper introduces a novel algorithm for automation of contextual text correction using a Linguistic Habit Graph (LHG) also introduced in this paper. A specialist internet crawler has been constructed for searching through web sites in order to build a Linguistic Habit Graph after text corpuses gathered in Polish web sites. The achieved correction results on a basis of this algorithm using this LHG were compared with commercial programs which also enable to make text correction: Microsoft Word 2007, Open Office Writer 3.0 and search engine Google. The achieved results of text correction were much better than correction made by these commercial tools.
