By Fuchun Sun, Jianwei Zhang, Jinde Cao, Wen Yu
The quantity set LNCS 5263/5264 constitutes the refereed complaints of the fifth foreign Symposium on Neural Networks, ISNN 2008, held in Beijing, China in September 2008.
The 192 revised papers awarded have been rigorously reviewed and chosen from a complete of 522 submissions. The papers are prepared in topical sections on computational neuroscience; cognitive technology; mathematical modeling of neural platforms; balance and nonlinear research; feedforward and fuzzy neural networks; probabilistic equipment; supervised studying; unsupervised studying; help vector computer and kernel tools; hybrid optimisation algorithms; desktop studying and knowledge mining; clever keep watch over 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 regulate with neural networks; nature encouraged tools of high-dimensional discrete info research; development acceptance and knowledge processing utilizing neural networks.
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Additional info for Advances in neural networks - ISNN 2008 5th International Symposium on Neural Networks, ISNN 2008, Beijing, China, September 24-28, 2008: proceedings
1, the cNTF Basis Functions Fig. 2. Tensor model for calculation of basis functions via cNTF cochlear power feature can be considered as neurons response in the inner hair-cells. The hair-cells have receptive fields which refer to a coding of sound frequency. Here we employ the sparse localized basis function A ∈ RNf ×R in time-frequency subspace to transform the auditory feature into the sparse feature subspace, where R is the dimension of sparse feature subspace. The representation of auditory sparse feature Xs is obtained via the following transformation: ˆ Xs = AX (15) Robust Speaker Modeling Based on Constrained Nonnegative Tensor Factorization 10 2 20 1 30 0 40 2 50 0 20 40 60 80 0 20 40 60 80 0 20 40 60 80 17 1 60 0 70 2 80 1 90 100 20 40 60 (a) Basis functions 80 0 (b) Examples of encoding vector Fig.
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.
B) Examples for encoding feature vector. e. A ˆ = [A−1 ]+ . Figure 3(a) where A shows an example of basis functions in spectro-temporal domain. From this result we can see that most elements of basis function are near to zero, which accords with the sparse constraint of cNTF. Figure 3(b) gives several examples for the encoding feature vector after transformation which also prove the sparse characteristic of feature. Our feature extraction model is based on the fact that in sparse coding the energy of the signal is concentrated on a few components only, while the energy of additive noise remains uniformly spreading on all the components.