By Francesco Camastra (auth.), Bruno Apolloni, Maria Marinaro, Giuseppe Nicosia, Roberto Tagliaferri (eds.)
This ebook constitutes the completely refereed postproceedings of the sixteenth Italian Workshop on Neural Nets, WIRN 2005, in addition to the satellite tv for pc overseas Workshop on ordinary and synthetic Immune platforms, NAIS 2005, held in Vietri sul Mare, Italy in June 2005.
The forty-one revised papers provided including a lecture by way of the winner of the Premio Caianiello award have been conscientiously reviewed and better in the course of rounds of choice and refereeing. The papers are prepared in topical sections on versions of neural networks, architectures and algorithms, sign processing, and development reputation, methodologies and functions of fuzzy and neurofuzzy computing, ordinary immune structures, and synthetic immune systems.
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5. In the same table is also reported the best generalization error reported in the StatLog report [8] for each problem, together with the rank scored by SNN when its generalization error is inserted into the list of available results. Table 1. 5. On the other hand, the complexity of SNN is considerably higher (except for the Shuttle problem). 5. It ranges from 3 sec. 5 requires at most three minutes. This behavior depends on the method employed to produce the implicants and the corresponding rules.
Denote with Qln the collection of the subsets of In with cardinality l: with Cn,l = Qln = {{j1 , j2 , . . , jl } : 1 ≤ j1 < j2 < · · · < jl ≤ n, ji ∈ In , for i = 1, . . , l} When the lexicographic ordering is used to compare the relative position of its elements (ordered in an increasing way), Qln becomes a chain. Consequently, the counter metric dc can be defined on Qln , being dc (B, C) the length of the subchain connecting B and C for every B, C ∈ Qln . According to this definition, if B = {1, 2, .
The final clustering is performed by applying the clustering algorithm Ccom to the main similarity matrix M . g. PMO and RS cluster ensembles). 4 Experimental Results In this section we present some preliminary experimental results with the RE-Clust ensemble algorithm. The Ward’s hierarchical agglomerative clustering algorithm [10] has been applied as “base” clustering algorithm. 1 Experimental Environment Synthetic Data Generation. We experimented with 2 different sample generators, whose samples are distributed according to different mixtures of high dimensional gaussian distributions.