By Shuyan Chen, Wei Wang (auth.), Jun Wang, Zhang Yi, Jacek M. Zurada, Bao-Liang Lu, Hujun Yin (eds.)
This ebook and its sister volumes represent the complaints of the 3rd overseas Symposium on Neural Networks (ISNN 2006) held in Chengdu in southwestern China in the course of may well 28–31, 2006. After a winning ISNN 2004 in Dalian and ISNN 2005 in Chongqing, ISNN grew to become a well-established sequence of meetings on neural computation within the quarter with transforming into acceptance and enhancing caliber. ISNN 2006 got 2472 submissions from authors in forty three international locations and areas (mainland China, Hong Kong, Macao, Taiwan, South Korea, Japan, Singapore, Thailand, Malaysia, India, Pakistan, Iran, Qatar, Turkey, Greece, Romania, Lithuania, Slovakia, Poland, Finland, Norway, Sweden, Demark, Germany, France, Spain, Portugal, Belgium, Netherlands, united kingdom, eire, Canada, united states, Mexico, Cuba, Venezuela, Brazil, Chile, Australia, New Zealand, South Africa, Nigeria, and Tunisia) throughout six continents (Asia, Europe, North the United States, South the United States, Africa, and Oceania). in line with rigorous experiences, 616 fine quality papers have been chosen for booklet within the lawsuits with the popularity fee being below 25%. The papers are geared up in 27 cohesive sections overlaying all significant issues of neural community study and improvement. as well as the various contributed papers, ten unusual students gave plenary speeches (Robert J. Marks II, Erkki Oja, Marios M. Polycarpou, Donald C. Wunsch II, Zongben Xu, and Bo Zhang) and tutorials (Walter J. Freeman, Derong Liu, Paul J. Werbos, and Jacek M. Zurada).
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Extra info for Advances in Neural Networks - ISNN 2006: Third International Symposium on Neural Networks, Chengdu, China, May 28 - June 1, 2006, Proceedings, Part III
The conditions for optimality lead to a set of linear equations: ⎡0 ⎢ ⎢l ⎢⎣ v ⎤ ⎥ ⎡b ⎤ ⎡0⎤ 1 ⎥⎢ ⎥ = ⎢ ⎥. Ω + I ⎣⎢α ⎦ ⎣ y ⎦ γ ⎥⎦ lTv (4) with y = [ y1 , ⋅⋅⋅, yl ], l v = [1, ⋅⋅⋅,1], α = [α1 , ⋅⋅⋅, α l ] . Ω = K (xi , x j ) is a kernel function satisfying Mercer’s conditions. Three typical kernel functions are listed in Table 1. The LS-SVM regression formulation is then constructed: l f ( x ) = ∑ α i K ( x, x i ) + b . (5) i =1 Table 1. Typical kernel functions Kernel function Expression Linear kernel xTi x Polynomial kernel (1 + xi T x) d RBF kernel exp(− x − xi 2 /σ 2 ) 3 Prediction of Railway Passenger Traffic Volume The prediction is conducted in the following way.
In this case, truncating the lowest 2, called the DC and the first AC component, gives the best result while truncating more performs worse due to the loss of some important low-frequency information. However, the optimal length that should be truncated is not of course a fixed value, since how the frequencies represent the features of an image varies with different cases. Table 1. 2 Variations in Feature Length These results are obtained after truncating the first 2 low-frequency components. Longer length basically gives lower error rate, that’s reasonable since more features contain more information that would be used for classification.
6 is a better choice. 4 Use PCA After Truncated DCT Regarding the truncated DCT coefficients as the raw feature, we can apply PCA to transform the vectors to a lower-dimensionality space where the features may be easier to discriminate. Fixing the final dimensionality, experiments are made on different lengths of truncated DCT coefficients (the feature lengths before PCA): Table 5. 3, length _ features = 10 Apparently, the longer the raw feature, the better the result, that’s easy to understand.