Download Artificial Neural Networks - ICANN 2010: 20th International by Konstantinos Diamantaras, Wlodek Duch, Lazaros S. Iliadis PDF

By Konstantinos Diamantaras, Wlodek Duch, Lazaros S. Iliadis

This 3 quantity set LNCS 6352, LNCS 6353, and LNCS 6354 constitutes the refereed court cases of the 20 th foreign convention on synthetic Neural Networks, ICANN 2010, held in Thessaloniki, Greece, in September 2010. The 102 revised complete papers, sixty eight brief papers and 29 posters offered have been rigorously reviewed and chosen from 241 submissions. the second one quantity is split in topical sections on Kernel algorithms – help vector machines, wisdom engineering and choice making, recurrent ANN, reinforcement studying, robotics, self organizing ANN, adaptive algorithms – platforms, and optimization.

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5 −1 −1 65 0 1 log10 σ 2 3 (d) Fig. 3. Percentage of KOs reduction in AccSO for different settings of C and σ parameters. The squared dots represent the values recommended in [8] for the dataset. R. Dorronsoro A. Finally, it is of interest to test whether different values for the SVM parameters C and σ would provide a different degree of improvement. 1, 1000]. We depict the percentage of reduction achieved for this range of values as contour maps in figure 3 for the datasets Breast, Flare, Image and Waveform, the rest of the datasets showing similar behaviours.

213 Table 3. 7 . Afterwards another 5-Fold Cross Validation optimization approach was performed by dividing the actual data records in two partitions according to the range of their expected output. 09% . 91% . 358 29 So by using a heuristic approach, the user can use his experience in the input data vectors in order to expect the output in the one of the two intervals of values. 2 . 45 . The models obtained by this research effort are quite promising and they produce RMSE values of a very low magnitude in the testing phase.

In fact, we have K ∇f (αt ) · v = δt−j ∇f (αt ) · dT −j j=1 K δt−j (∇f (αt )U − yUt−j yLt−j ∇f (αt )L ) = j=1 which can be computed without needing any KO. Once we have decided to perform an update in the v direction, observe that the value of the objective function will change as f (α + λv) = f (α) + 12 λ2 v T Qv + δv T Qα − δv · α, and so the optimal stepsize ignoring constraints can be obtained as λo = −∂2 −v · ∇f (α) = T . v T Qv v Qv (5) As v is sparse by construction, at most 2K entries are non-zero, and so v T Qv can be computed efficiently by defining Ri = Qi v = vj =0 Qij vj and using v T Qv = vj =0 vj Rj , which requires at most 2KN kernel operations.

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