Titlebook: Neural Information Processing; 21st International C Chu Kiong Loo,Keem Siah Yap,Kaizhu Huang Conference proceedings 2014 Springer Internati

萬圣節(jié)

書目名稱Neural Information Processing影響因子(影響力)




書目名稱Neural Information Processing影響因子(影響力)學(xué)科排名




書目名稱Neural Information Processing網(wǎng)絡(luò)公開度




書目名稱Neural Information Processing網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Neural Information Processing被引頻次




書目名稱Neural Information Processing被引頻次學(xué)科排名




書目名稱Neural Information Processing年度引用




書目名稱Neural Information Processing年度引用學(xué)科排名




書目名稱Neural Information Processing讀者反饋




書目名稱Neural Information Processing讀者反饋學(xué)科排名

數(shù)量

Neural Information Processing978-3-319-12640-1Series ISSN 0302-9743 Series E-ISSN 1611-3349

FAZE

Lecture Notes in Computer Sciencehttp://image.papertrans.cn/n/image/663571.jpg

觀察

https://doi.org/10.1007/978-3-319-12640-1activity recognition; artificial intelligence; big data; bio-inspired computing; brain-computer interfac

HEDGE

Non-negative Matrix Factorization with Schatten p-norms Reguralizationlarization terms were previously added to the NMF objective function in order to produce sparser results and thus to obtain a more qualitative partition of data. We would like to propose the general framework for regularized NMF based on Schatten p-norms. Experimental results show the effectiveness of our approach on different data sets.

GIST

A New Energy Model for the Hidden Markov Random Fieldsood energy function of the Hidden Markov Random Fields model based on the Hidden Markov Model formalism. With this new energy model, we aim at (1) avoiding the use of a key parameter chosen empirically on which the results of the current models are heavily relying, (2) proposing an information rich modelisation of neighborhood relationships.

合唱隊(duì)

Graph Kernels Exploiting Weisfeiler-Lehman Graph Isomorphism Test Extensionsing phase of the nodes based on test-specific information extracted from the graph, for example the set of neighbours of a node. We defined a novel relabelling and derived two kernels of the framework from it. The novel kernels are very fast to compute and achieve state-of-the-art results on five real-world datasets.

Polydipsia

A New Ensemble Clustering Method Based on Dempster-Shafer Evidence Theory and Gaussian Mixture Modelsults from single clustering methods. We introduce the GMM technique to determine the confidence values for candidate results from each clustering method. Then we employ the DS theory to combine the evidences supplied by different clustering methods, based on which the final result is obtained. We t

Creatinine-Test

Extraction of Dimension Reduced Features from Empirical Kernel Vectorping to make the trained classifier by using the linear SVM with the extracted feature vectors equivalent to the one obtained by the standard kernel SVM. The proposed feature extraction mapping is defined by using the eigen values and eigen vectors of the Gram matrix. Since the eigen vector problem

疲憊的老馬

Method of Evolving Non-stationary Multiple Kernel Learningimal mapping model in a large high-dimensional feature space. However, it is not suitable to compute the composite kernel in a stationary way for all samples. In this paper, we propose a method of evolving non-stationary multiple kernel learning, in which base kernels are encoded as tree kernels and