Download Artificial Neural Networks – ICANN 2010: 20th International by Elina Parviainen (auth.), Konstantinos Diamantaras, Wlodek PDF
By Elina Parviainen (auth.), Konstantinos Diamantaras, Wlodek Duch, Lazaros S. Iliadis (eds.)
th This quantity is a part of the three-volume lawsuits of the 20 overseas convention on Arti?cial Neural Networks (ICANN 2010) that was once held in Th- saloniki, Greece in the course of September 15–18, 2010. ICANN is an annual assembly subsidized by means of the ecu Neural community Society (ENNS) in cooperation with the foreign Neural community So- ety (INNS) and the japanese Neural community Society (JNNS). This sequence of meetings has been held every year on the grounds that 1991 in Europe, masking the ?eld of neurocomputing, studying platforms and different similar parts. As long ago 19 occasions, ICANN 2010 supplied a extraordinary, vigorous and interdisciplinary dialogue discussion board for researches and scientists from world wide. Ito?eredagoodchanceto discussthe latestadvancesofresearchandalso the entire advancements and functions within the region of Arti?cial Neural Networks (ANNs). ANNs offer a data processing constitution encouraged by means of biolo- cal anxious platforms they usually include a lot of hugely interconnected processing components (neurons). each one neuron is a straightforward processor with a constrained computing means in most cases constrained to a rule for combining enter indications (utilizing an activation functionality) for you to calculate the output one. Output signalsmaybesenttootherunitsalongconnectionsknownasweightsthatexcite or inhibit the sign being communicated. ANNs have the option “to examine” through instance (a huge quantity of circumstances) via a number of iterations with no requiring a priori ?xed wisdom of the relationships among approach parameters.
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Extra info for Artificial Neural Networks – ICANN 2010: 20th International Conference, Thessaloniki, Greece, September 15-18, 2010, Proceedings, Part III
Component parameters θmn in chess-board arrangement corresponding to the typical variants of samples (|Mω | = 50). The parameters θnm are displayed by greylevels inversely (0 = white, 1 = black). The diﬀerent ”style” of patterns of both classes is well apparent. the mixture model, (|Mω | = 1, 2, 5, 10, 20, 50, 100, 200, 500) and the size of the training set (|Sω | = 103 , 104 , 105 ). For the sake of independent testing we have used one pair of suﬃciently large sets (2 × 105 patterns for each class).
In light of the above, we can conclude that the classiﬁer FVQIT is the best classiﬁer to be combined with discretizers and ﬁlters to deal with problems with a much higher number of features than instances, such as DNA microarray geneexpression problems. 4 Conclusions and Future Work In this paper, eight DNA microarray gene-expression datasets have been classiﬁed using a method that combines a discretizator, a ﬁlter and a local classiﬁer called FVQIT. The classiﬁer used is capable of obtaining complex classiﬁcation models by constructing a piecewise borderline between the diﬀerent regions and then classifying locally by using one-layer neural networks.
The training data was chosen to be particularly challenging by using backgrounds containing similar or the same features as the object itself. As demonstrated, the system initially starts with a bag-of-features approach which gives very low recognition rates. During training, the system learns the typical feature arrangement of the object, however, and increases recognition rates to close to 100%. The system does hereby not use any prior knowledge about the feature arrangement. , displayed in Fig.