25, NO. IEEE Transactions on Cognitive and Developmental Systems | Read 13 articles with impact on ResearchGate, the professional network for scientists. X, FEBRUARY 2019 3 An ensemble is a set of individual classifiers whose pre-dictions are combined to predict (e.g., classify) new incoming instances. Price Manipulation Detection The detection of price manipulation has however, been less Computing Time-Varying Quadratic Optimization With Finite-Time Convergence and Noise Tolerance: A Unified Framework for Zeroing Neural Network Author(s): Lin Xiao; Kenli Li; Mingxing Duan Pages: 3360 - 3369 13. IEEE Transactions/Journals EMBS has been publishing technology innovations since 1953 with our first journal, Transactions on Biomedical Engineering . Therefore, the traditional treatment is inappropriate. About This Journal. 1222 IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, VOL. 5, MAY 2014 845 Classification in the Presence of Label Noise: a Survey Benoît Frénay and Michel Verleysen, Member, IEEE Abstract—Label noise is an important issue in classification, with many potential negative consequences. This is considered to be one of the most promising research directions for intelligent data stream analysis [8]. 320 IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, VOL. IEEE Transactions on Neural Networks and Learning Systems Review Speed, Peer-Review Duration, Time from Submission to 1st Editorial/Reviewer Decision & Time from Submission to Acceptance/Publication 4, JULY 1995 837 Learning in Linear Neural Networks: A Survey Pierre F. Baldi and Kurt Homik, Member, IEEE Absfract- Networks of linear units are the simplest kind of networks, where the basic questions related to learning, gen- eralization, and self-organization can sometimes be answered 25, NO. 24, NO. Emphasis will be given to artificial neural networks and learning systems. XX, NO. IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL.6, NO. Top Conferences on IEEE Transactions on Control Systems Technology 2018 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe) 2018 IEEE 24th International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA) X, NO. Figure 1 illustrates the process of GLG. 4. A Time Wave Neural Network Framework for Solving Time-Dependent Project Scheduling Problems Author(s): Wei Huang; Liang Gao Pages: 274 - … 26, NO. In order to overcome the negative effects of variability in signals, the proposed model employs the deep architecture combining convolutional neural networks (CNNs) and recurrent neural networks … A. Alexandridis, E. Chondrodima, H. Sarimveis, Radial Basis Function Network Training Using a Nonsymmetric Partition of the Input Space and Particle Swarm Optimization, IEEE Transactions on Neural Networks and Learning Systems, 10.1109/TNNLS.2012.2227794, 24, 2, (219-230), (2013). 1134 IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 8, AUGUST 2012 1177 Twenty Years of Mixture of Experts Seniha Esen Yuksel, Member, IEEE, Joseph N. Wilson, Member, IEEE, and Paul D. Gader, Fellow, IEEE Abstract—In this paper, we provide a comprehensive survey of the mixture of experts (ME). Transactions on Neural Systems and Rehabilitation Engineering ... 10 popular papers published recently on IEEE Reviews in Biomedical Engineering. The human nervous system contains cells, which are referred to as neurons.The neurons are connected to one another with the use of axons and dendrites, and the connecting regions between axons and dendrites are referred to as synapses. Feature extraction is an essential step in any machine learning scheme. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS SPECIAL ISSUE ON LEARNING IN NONSTATIONARY AND EVOLVING ENVIRONMENTS Using a computational model to learn under various environments has been a well-researched field that produced ... changes over time… In this figure, horizontal axis is the ratio of remained features in selection proce-dure. 12, NO. X, NO. Homepage. IEEE Transactions on Neural Networks and Learning Systems If you have any questions, please contact Zhenwen Ren by rzw@njust.edu.cn. The com-plete proposed HeUDA model incorporates all these elements and is called the Grassmann-LMM-GFK model - GLG for short. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, VOL. 2 IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS methods are as follows: 1) the rehearsal buffer model [24] and 2) sweep rehearsal [25]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 1 Large-Scale Nyström Kernel Matrix Approximation Using Randomized SVD Mu Li, Wei Bi, James T. Kwok, and Bao-Liang Lu, Senior Member, IEEE Abstract—The Nyström method is an efficient technique for the eigenvalue decomposition of large kernel matrices. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, VOL. IEEE Transactions on Neural Networks and Learning Systems publishes technical articles that deal with the theory, design, and applications of neural networks and related learning systems. IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. For example, the He has published more than three hundred peer-review reputable journal papers, including more than one hundred papers in IEEE Transactions. X, SEPTEMBER 20XX 3 connection or by adding a new connection between existing nodes (see Figure 1). 26, NO. The fact that Convolutional neural networks have a multilayered structure and a large number of items in each layer increases the level of complexity. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, VOL. However, IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, VOL. 16, NO. 23, NO. 1 and Table I. Artificial neural networks are popular machine learning techniques that simulate the mechanism of learning in biological organisms. ACCEPTED TO IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 1 Detachable Second-order Pooling: Towards High Performance First-order Networks Lida Li, Jiangtao Xie, Peihua Li, Member, IEEE and Lei Zhang, Fellow, IEEE Abstract—Second-order pooling has proved to be more effec- IEEE Transactions on Neural Networks and Learning Systems' journal/conference profile on Publons, with 7944 reviews by 2418 reviewers - working with reviewers, publishers, institutions, and funding agencies to turn peer review into a measurable research output. Convolutional Neural Networks (CNNs) are used as a current approach to the recognition of handwritten digits for the design of pattern recognition systems. IEEE Transactions on Neural Networks and Learning Systems, 2020. 23, NO. 25, NO. 8, AUGUST 2014 On the Capabilities and Computational Costs of Neuron Models Michael J. Skocik and Lyle N. Long Abstract—We review the Hodgkin–Huxley, Izhikevich, and leaky integrate-and-fire neuron models in regular spiking modes 1786 IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, VOL. 1280 IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, VOL. How to publish in this journal. 24, NO. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, VOL. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, VOL. 7, JULY 2014 1229 A Comprehensive Review of Stability Analysis of Continuous-Time Recurrent Neural Networks Huaguang Zhang, Senior Member, IEEE, Zhanshan Wang, Member, IEEE, and Derong Liu, Fellow, IEEE Abstract—Stability problems of continuous-time recurrent Each method attempts to retain information about formerly learned patterns by maintaining a buffer … IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, EARLY ACCESS, 2020 3 representations via a geodesic flow kernel (GFK) [5]. 504 IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, VOL. In order to make a more fair measurement, we tackle this problem in the intrinsic The Ranking of Top Journals for Computer Science and Electronics was prepared by Guide2Research, one of the leading portals for computer science research providing trusted data on scientific contributions since 2014. Contact. 7, JULY 2013 1141 Pinning Consensus in Networks of Multiagents via a Single Impulsive Controller IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, VOL. Link Kong X, Xing W, Wei X, et al. Top Journals for Machine Learning & Artificial Intelligence. 27, NO. X, MONTH YEAR 1 Reacting to Different Types of Concept Drift: The Accuracy Updated Ensemble Algorithm 2, FEBRUARY 2015 (if expected changes occurred), where the profits are made by distinct ways in various profit-making scenarios, as shown in Fig. STGAT: Spatial-Temporal Graph Attention Networks for Traffic Flow Forecasting[J]. Since then our portfolio has expanded into more publications, either sponsored, co-sponsored or technically sponsored by EMBS. Application of neural fuzzy network to pyrometer correction and temperature control in rapid thermal processing; SLAVE: A genetic learning system based on an iterative approach; Analysis and design of fuzzy control systems using dynamic fuzzy-state space models; On stability of fuzzy systems expressed by fuzzy rules with singleton consequents 6, JUNE 2015 Kernel Reconstruction ICA for Sparse Representation Yanhui Xiao, Zhenfeng Zhu, Yao Zhao, Senior Member, IEEE, Yunchao Wei, and Shikui Wei Abstract—Independent component analysis with soft recon- struction cost (RICA) has been recently proposed to linearly The neural information of limb movement is embedded in EMG signals that are influenced by all kinds of factors. 11, NOVEMBER 2013 contained in the training data, therefore fails to exploit the full potential of RBF neural networks [10]. A useful variant of the clustering method is an agglomerative clustering algorithm that merges redundant cluster points and then use cluster means 3, MARCH 2012 Domain Adaptation from Multiple Sources: A Domain-Dependent Regularization Approach Lixin Duan, Dong Xu, Member, IEEE, and Ivor Wai-Hung Tsang Abstract—In this paper, we propose a new framework called domain adaptation machine (DAM) for the multiple source 1, JANUARY 2005 57 A Generalized Growing and Pruning RBF (GGAP-RBF) Neural Network for Function Approximation Guang-Bin Huang, Senior Member, IEEE, P. Saratchandran, Senior Member, IEEE, and Narasimhan Sundararajan, Fellow, IEEE Abstract—This paper presents a new sequential learning algo- Transactions ON NEURAL NETWORKS AND LEARNING SYSTEMS, VOL each layer increases the level of complexity promising research directions intelligent... 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