By Guosheng Hu, Liang Hu, Jing Song, Pengchao Li, Xilong Che, Hongwei Li (auth.), Liqing Zhang, Bao-Liang Lu, James Kwok (eds.)
This e-book and its sister quantity acquire refereed papers awarded on the seventh Inter- tional Symposium on Neural Networks (ISNN 2010), held in Shanghai, China, June 6-9, 2010. development at the good fortune of the former six successive ISNN symposiums, ISNN has develop into a well-established sequence of well known and top of the range meetings on neural computation and its purposes. ISNN goals at delivering a platform for scientists, researchers, engineers, in addition to scholars to assemble jointly to offer and talk about the newest progresses in neural networks, and purposes in varied parts. these days, the sector of neural networks has been fostered a ways past the conventional man made neural networks. This 12 months, ISNN 2010 bought 591 submissions from greater than forty international locations and areas. in accordance with rigorous experiences, a hundred and seventy papers have been chosen for ebook within the complaints. The papers accumulated within the complaints conceal a extensive spectrum of fields, starting from neurophysiological experiments, neural modeling to extensions and purposes of neural networks. we now have geared up the papers into volumes according to their subject matters. the 1st quantity, entitled “Advances in Neural Networks- ISNN 2010, half 1,” covers the subsequent themes: neurophysiological starting place, conception and versions, studying and inference, neurodynamics. the second one quantity en- tled “Advance in Neural Networks ISNN 2010, half 2” covers the next 5 issues: SVM and kernel tools, imaginative and prescient and photograph, info mining and textual content research, BCI and mind imaging, and applications.
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Additional info for Advances in Neural Networks - ISNN 2010: 7th International Symposium on Neural Networks, ISNN 2010, Shanghai, China, June 6-9, 2010, Proceedings, Part II
In: Proc. of the 2008 International Conference on Data Mining (DMIN 2008), July 2008, vol. II, pp. 590–596 (2008) 21. : Computational Intelligence: An Introduction, 2nd edn. cn Abstract. Kernel matching pursuit (KMP), as a greedy machine learning algorithm, appends iteratively functions from a kernel-based dictionary to its solution. An obvious problem is that all kernel functions in dictionary will keep unchanged during the whole process of appending. It is difficult, however, to determine the optimal dictionary of kernel functions ahead of training, without enough prior knowledge.
Experimental results demonstrated that ACO-SVR worked better than SVR optimized by trial-and-error procedure (T-SVR) and back-propagation neural network (BPNN). Keywords: Grid resources prediction, Support vector regression, Ant Colony Optimization. 1 Introduction The Grid Computing tries to enable all kinds of resources or services being shared across the Internet. In the grid environment, the availability of grid resources vary over time and such changes will affect the performance of the tasks running on the grid.
Thus, the small size of kernel matrix makes the computation and storage possible. , φ(ΣM )) in feature space. The covariance matrix is given as follows: C= 1 M M φ(Σi )φ(Σi )T , (7) i=1 It also accords with the eigen-equation: Cν = λν, (8) Where ν and λ are corresponding eigenvector and eigenvalue of covariance matrix. The eigenvector is now expanded using all the projected matrix Φ(Σ) as: M αi φ(Σi ), ν= (9) i=1 By substituting Eq. 7, Eq. 9 into Eq. 8, we can get the following formula: Kα = Mλα, (10) where α is span coefficient, K is Gram matrix denoted as K = Φ(Σ)T Φ(Σ) = (κij )1≤i≤M,1≤j≤M .