Download Artificial Neural Networks – ICANN 2010: 20th International by Shigeo Abe, Ryousuke Yabuwaki (auth.), Konstantinos PDF

By Shigeo Abe, Ryousuke Yabuwaki (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 backed by way of the eu Neural community Society (ENNS) in cooperation with the overseas Neural community So- ety (INNS) and the japanese Neural community Society (JNNS). This sequence of meetings has been held every year for the reason that 1991 in Europe, protecting the ?eld of neurocomputing, studying platforms and different similar parts. As some time past 19 occasions, ICANN 2010 supplied a distinctive, full of life and interdisciplinary dialogue discussion board for researches and scientists from worldwide. Ito?eredagoodchanceto discussthe latestadvancesofresearchandalso all of the advancements and functions within the sector of Arti?cial Neural Networks (ANNs). ANNs supply a data processing constitution encouraged via biolo- cal frightened structures they usually encompass a great number of hugely interconnected processing components (neurons). every one neuron is an easy processor with a restricted computing means regularly constrained to a rule for combining enter signs (utilizing an activation functionality) with a view to calculate the output one. Output signalsmaybesenttootherunitsalongconnectionsknownasweightsthatexcite or inhibit the sign being communicated. ANNs find a way “to study” by way of instance (a huge quantity of situations) via a number of iterations with no requiring a priori ?xed wisdom of the relationships among approach parameters.

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Extra resources for Artificial Neural Networks – ICANN 2010: 20th International Conference, Thessaloniki, Greece, September 15-18, 2010, Proceedings, Part II

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Es Abstract. Second order SMO represents the state–of–the–art in SVM training for moderate size problems. t just a pair of multipliers. In this paper we will illustrate how SMO works in a two stage fashion, setting first the values of the bounded multipliers to the penalty factor C and proceeding then to adjust the non–bounded multipliers. Furthermore, during this second stage the selected pairs for update often appear repeatedly during the algorithm. Taking advantage of this, we shall propose a procedure to combine previously used descent directions that results in much fewer iterations in this second stage and that may also lead to noticeable savings in kernel operations.

1. Evolution of the number of bounded and unbounded α coefficients for every dataset. The x-axis represents the percentage of iterations performed by the algorithm (in logarithmic scale), while the y-axis stands for the number of upper bounded or unbounded coefficients. Faster Directions for Second Order SMO 35 Algorithm 1. Accelerated SMO 1: initialize α = 0, ∇f (α) = 0p, Q = ∅ ; 2: while (stopping condition == FALSE) do 3: find (L, U ) second (4) order SMO rules ; 4: if pair (L, U ) is found in Q then 5: build accelerating direction v ; 6: if v is feasible and ∂2 < ∂1 then 7: compute R, optimal unbounded stepsize λo using (5) ; 8: clip λo to meet constraints → λ∗ ; 9: α = α + λ∗ v, ∇f (α) = ∇f (α) + λ∗ R, Q = ∅ ; 10: else 11: remove (L, U ) and previous updates from Q ; 12: perform standard SMO update using (L, U ), add (L, U ) to Q ; 13: end if 14: else 15: perform standard SMO update using (L, U ), add (L, U ) to Q ; 16: end if 17: end while the number of upper bounded and unbounded multipliers becomes stable, and only slight changes in their numbers are made until the end of the algorithm.

MIT Press, Cambridge (1999) 3. : Improvements to Platt’s SMO Algorithm for SVM Classifier Design. Neural Computation 13(3), 637–649 (2001) 4. : Svmtorch: Support vector machines for large-scale regression problems. Journal of Machine Learning Research 1, 143–160 (2001) 5. : LIBSVM: a Library for Support Vector Machines (2001) 6. : Nonlinear Programming: Theory and Algorithms. Wiley-Interscience Series in Discrete Mathematics and Optimization. Wiley, Chichester (1992) 7. : Working Set Selection using Second Order Information for Training Support Vector Machines.

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