By Anastassia Angelopoulou, Alexandra Psarrou, José García-Rodríguez (auth.), Ignacio Rojas, Gonzalo Joya, Joan Cabestany (eds.)
This two-volume set LNCS 7902 and 7903 constitutes the refereed lawsuits of the twelfth foreign Work-Conference on synthetic Neural Networks, IWANN 2013, held in Puerto de los angeles Cruz, Tenerife, Spain, in June 2013. The 116 revised papers have been rigorously reviewed and chosen from various submissions for presentation in volumes. The papers discover sections on mathematical and theoretical tools in computational intelligence, neurocomputational formulations, studying and model emulation of cognitive capabilities, bio-inspired platforms and neuro-engineering, complex issues in computational intelligence and applications.
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Additional resources for Advances in Computational Intelligence: 12th International Work-Conference on Artificial Neural Networks, IWANN 2013, Puerto de la Cruz, Tenerife, Spain, June 12-14, 2013, Proceedings, Part II
The information-criterion MDL of Rissanen , is deﬁned as: 1 M DL(K) = − ln[L(X|WK , ΘK )] + M ln(N ) 2 where (6) N p(xi |WK , ΘK ) L(X|WK , ΘK ) = max (7) i=1 The ﬁrst term − ln[L(X|WK , ΘK )] measures the model probability with respect to the model parameter WK , ΘK deﬁned for a Gaussian mixture by the mixture coeﬃcients WK and d-dimensional means and covariances ΘK . The second term 1 2 M ln(N ) measures the number of free parameters needed to encode the model and serves as a penalty for models that are too complex.
The search starts generating an initial schedule, σ, by assigning each task t ∈ Ω to its nearest quay crane (line 1). The value of the parameter k is also set to 1 (line 4). A shaking procedure allows to reach unexplored regions of the search space by means of the reassignment of k tasks to another quay crane. The reassigned tasks are selected on the basis of a frequency memory. In this way, at each step, a neighbour schedule, σ , is generated at random from σ within the neighbourhood structure Nk (line 6).
Of the IEEE Conference on Computer Vision and Pattern Recognition, vol. I, pp. 878–885 (2005) 12. : Unsupervised improvement of visual detectors using co-training. In: Proc. of the IEEE International Conference on Computer Vision, vol. I, pp. 626–633 (2003) 13. : Topology Representing Networks. The Journal of Neural Networks 7(3), 507–522 (1994) 14. : Segmentation by fusion of histogram-based k-means clusters in diﬀerent color spaces. IEEE Trans. on Image Processing 5(17), 780–787 (2008) 10 A. Angelopoulou et al.