By Fuchun Sun, Jianwei Zhang, Jinde Cao, Wen Yu
The quantity set LNCS 5263/5264 constitutes the refereed complaints of the fifth overseas Symposium on Neural Networks, ISNN 2008, held in Beijing, China in September 2008.
The 192 revised papers offered have been conscientiously reviewed and chosen from a complete of 522 submissions. The papers are prepared in topical sections on computational neuroscience; cognitive technological know-how; mathematical modeling of neural platforms; balance and nonlinear research; feedforward and fuzzy neural networks; probabilistic equipment; supervised studying; unsupervised studying; help vector computing device and kernel equipment; hybrid optimisation algorithms; computing device studying and information mining; clever keep an eye on and robotics; development reputation; audio photo processinc and laptop imaginative and prescient; fault prognosis; purposes and implementations; purposes of neural networks in digital engineering; mobile neural networks and complicated keep watch over with neural networks; nature encouraged equipment of high-dimensional discrete info research; development acceptance and data processing utilizing neural networks.
Read or Download Advances in neural networks - ISNN 2008 5th International Symposium on Neural Networks, ISNN 2008, Beijing, China, September 24-28, 2008: proceedings PDF
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Additional resources for Advances in neural networks - ISNN 2008 5th International Symposium on Neural Networks, ISNN 2008, Beijing, China, September 24-28, 2008: proceedings
The original signal was shown at the uppermost panel in the left column. Left column showed the decomposed signal reflecting the time course of the signal in the respective frequency band. Right column showed the reconstructed single-trial signal by the sum of a6 and d6. 6 L. Zou et al. Fig. 3. Sample results for the time-frequency plot of a single trial of VEP. Left column corresponding the original signal. Right column corresponding the reconstructed single-trial signal by wavelet transform. noticeable in the time-frequency distribution of the wavelet-based VEP estimate, whereas such activity can hardly be seen from the raw signal.
Zhang, and G. Shi about sparse coding and representation for sound and speech[8,9,10] is also proved to be useful for auditory modeling and speech separation which will be a potential way for robust speech feature extraction. As a powerful data modeling tool for pattern recognition, multilinear algebra of the higher order tensors has been proposed as a potent mathematical framework to manipulate the multiple factors underlying the observations. Currently common tensor decomposition methods include: (1) the CANDECOMP/PARAFAC model [11,12,13]; (2) the Tucker Model[14,15]; (3) Nonnegative Tensor Factorization (NTF) which imposes the nonnegative constraint on the CANDECOMP/PARAFAC model [16,17].
NM , where 1 ≤ nd ≤ Nd and 1 ≤ d ≤ M . ×NM rearranges the elements of X to form the matrix X(d) ∈ RNd ×Nd+1 Nd+2 ···NM N1 ···Nd−1 , which is the ensemble of vectors in RNd obtained by keeping index nd fixed and varying the other indices. Matricizing a tensor is similar to vectoring a matrix. The PARAFAC model was suggested independently by Carroll and Chang under the name CANDECOMP(canonical decomposition) and by Harshman under the name PARAFAC(parallel factor analysis) which has gained increasing attention in the data mining field.