全部文献期刊学位论文会议报纸专利标准年鉴图书|学者科研项目
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作者:Shifei Ding , Lingheng Meng ...
来源:[J].Cognitive Computation(IF 0.867), 2017, Vol.9 (2), pp.194-206Springer
摘要:Binding problem, which is also called feature binding, is primarily about integrating distributed information scattered on different cortical areas in a reasonable way. As a key problem in cognitive science and neuroscience, this concept is increasingly becoming a focus of consci...
作者:Shifei Ding , Zhibin Zhu , Xiekai Zhang
来源:[J].Neural Computing and Applications(IF 1.168), 2017, Vol.28 (5), pp.969-978Springer
摘要:Support vector machine (SVM) is a machine learning method based on statistical learning theory. It has a lot of advantages, such as solid theoretical foundation, global optimization, the sparsity of the solution, nonlinear and generalization. The standard form of SVM only applies...
作者:Shifei Ding , Nan Zhang ...
来源:[J].Neural Computing and Applications(IF 1.168), 2017, Vol.28 (11), pp.3119-3130Springer
摘要:Twin support vector machine (TWSVM) has gained increasing interest from various research fields recently. In this paper, we aim to report the current state of the theoretical research and practical advances on TWSVM. We first give the basic thought and theory of TWSVM, including ...
作者:Shifei Ding , Lili Guo , Yanlu Hou
来源:[J].Neural Computing and Applications(IF 1.168), 2017, Vol.28 (8), pp.1975-1984Springer
摘要:Extreme learning machine (ELM) proposed by Huang et al. is a learning algorithm for single-hidden layer feedforward neural networks (SLFNs). ELM has the advantage of fast learning speed and high efficiency, so it brought into public focus. Later someone developed regularized...
作者:Shifei Ding , Yuhu Cheng
来源:[J].Neural Computing and Applications(IF 1.168), 2017, Vol.28 (1), pp.671-681Springer
摘要:In this paper, a review of parameter optimization methods of pulse-coupled neural networks (PCNNs) is presented. Considering that PCNN has been used in image processing for many years, the aim of this paper was to provide an overview of the work that has been done and to ser...
作者:Hongjie Jia , Shifei Ding , Mingjing Du
来源:[J].Soft Computing(IF 1.124), 2017, Vol.21 (19), pp.5815-5827Springer
摘要:Spectral clustering will map the data points of the original space into a low-dimensional eigen-space to make them linearly separable, so it is able to process the data with complex structures. However, spectral clustering needs to store the entire similarity matrix and requ...
作者:Shifei Ding , Nan Zhang ...
来源:[J].International Journal of Machine Learning and Cybernetics, 2017, Vol.8 (2), pp.587-595Springer
摘要:Extreme learning machine (ELM) is not only an effective classifier but also a useful cluster. Unsupervised extreme learning machine (US-ELM) gives favorable performance compared to state-of-the-art clustering algorithms. Extreme learning machine as an auto encoder (ELM-AE) can ob...
作者:Lingheng Meng , Shifei Ding , Yu Xue
来源:[J].International Journal of Machine Learning and Cybernetics, 2017, Vol.8 (5), pp.1719-1729Springer
摘要:Autoencoder can learn the structure of data adaptively and represent data efficiently. These properties make autoencoder not only suit huge volume and variety of data well but also overcome expensive designing cost and poor generalization. Moreover, using autoencoder in deep lear...
作者:Nan Zhang , Shifei Ding
来源:[J].Memetic Computing, 2017, Vol.9 (2), pp.129-139Springer
摘要:Extreme learning machine (ELM) not only is an effective classifier in supervised learning, but also can be applied on unsupervised learning and semi-supervised learning. The model structure of unsupervised extreme learning machine (US-ELM) and semi-supervised extreme learning mac...

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