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# IEEE Transactions on Neural Networks and Learning Systems

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Displaying Results 1 - 23 of 23

Publication Year: 2013, Page(s): C1
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• ### IEEE Transactions on Neural Networks and Learning Systems publication information

Publication Year: 2013, Page(s): C2
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• ### Editorial A Successful Change From TNN to TNNLS and a Very Successful Year

Publication Year: 2013, Page(s):1 - 7
Cited by:  Papers (3)
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• ### Mixture Subclass Discriminant Analysis Link to Restricted Gaussian Model and Other Generalizations

Publication Year: 2013, Page(s):8 - 21
Cited by:  Papers (17)
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In this paper, a theoretical link between mixture subclass discriminant analysis (MSDA) and a restricted Gaussian model is first presented. Then, two further discriminant analysis (DA) methods, i.e., fractional step MSDA (FSMSDA) and kernel MSDA (KMSDA) are proposed. Linking MSDA to an appropriate Gaussian model allows the derivation of a new DA method under the expectation maximization (EM) frame... View full abstract»

• ### Density-Preserving Sampling: Robust and Efficient Alternative to Cross-Validation for Error Estimation

Publication Year: 2013, Page(s):22 - 34
Cited by:  Papers (5)  |  Patents (1)
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Estimation of the generalization ability of a classification or regression model is an important issue, as it indicates the expected performance on previously unseen data and is also used for model selection. Currently used generalization error estimation procedures, such as cross-validation (CV) or bootstrap, are stochastic and, thus, require multiple repetitions in order to produce reliable resu... View full abstract»

• ### Two-Stage Nonnegative Sparse Representation for Large-Scale Face Recognition

Publication Year: 2013, Page(s):35 - 46
Cited by:  Papers (46)
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This paper proposes a novel nonnegative sparse representation approach, called two-stage sparse representation (TSR), for robust face recognition on a large-scale database. Based on the divide and conquer strategy, TSR decomposes the procedure of robust face recognition into outlier detection stage and recognition stage. In the first stage, we propose a general multisubspace framework to learn a r... View full abstract»

• ### Projection-Pursuit-Based Method for Blind Separation of Nonnegative Sources

Publication Year: 2013, Page(s):47 - 57
Cited by:  Papers (9)
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This paper presents a projection pursuit (PP) based method for blind separation of nonnegative sources. First, the available observation matrix is mapped to construct a new mixing model, in which the inaccessible source matrix is normalized to be column-sum-to-1. Then, the PP method is proposed to solve this new model, where the mixing matrix is estimated column by column through tracing the proje... View full abstract»

• ### Synchronization for Coupled Neural Networks With Interval Delay: A Novel Augmented Lyapunov–Krasovskii Functional Method

Publication Year: 2013, Page(s):58 - 70
Cited by:  Papers (38)
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This paper is concerned with the synchronization problems for an array of neural networks with hybrid coupling and interval time-varying delay. First, a novel augmented Lyapunov-Krasovskii functional (LKF) method is proposed to develop delay-dependent synchronization criteria for the networks, which makes use of more relaxed conditions by employing the new type of augmented matrices with Kronecker... View full abstract»

• ### Observer-Based Adaptive Neural Network Control for Nonlinear Stochastic Systems With Time Delay

Publication Year: 2013, Page(s):71 - 80
Cited by:  Papers (118)
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This paper considers the problem of observer-based adaptive neural network (NN) control for a class of single-input single-output strict-feedback nonlinear stochastic systems with unknown time delays. Dynamic surface control is used to avoid the so-called explosion of complexity in the backstepping design process. Radial basis function NNs are directly utilized to approximate the unknown and desir... View full abstract»

• ### Qualitative Adaptive Reward Learning With Success Failure Maps: Applied to Humanoid Robot Walking

Publication Year: 2013, Page(s):81 - 93
Cited by:  Papers (16)
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In the human brain, rewards are encoded in a flexible and adaptive way after each novel stimulus. Neurons of the orbitofrontal cortex are the key reward structure of the brain. Neurobiological studies show that the anterior cingulate cortex of the brain is primarily responsible for avoiding repeated mistakes. According to vigilance threshold, which denotes the tolerance to risks, we can differenti... View full abstract»

• ### Novel Z-Domain Precoding Method for Blind Separation of Spatially Correlated Signals

Publication Year: 2013, Page(s):94 - 105
Cited by:  Papers (8)
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In this paper, we address the problem of blind separation of spatially correlated signals, which is encountered in some emerging applications, e.g., distributed wireless sensor networks and wireless surveillance systems. We preprocess the source signals in transmitters prior to transmission. Specifically, the source signals are first filtered by a set of properly designed precoders and then the co... View full abstract»

• ### Local Coordinates Alignment With Global Preservation for Dimensionality Reduction

Publication Year: 2013, Page(s):106 - 117
Cited by:  Papers (12)
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Dimensionality reduction is vital in many fields, and alignment-based methods for nonlinear dimensionality reduction have become popular recently because they can map the high-dimensional data into a low-dimensional subspace with the property of local isometry. However, the relationships between patches in original high-dimensional space cannot be ensured to be fully preserved during the alignment... View full abstract»

• ### Hopf Bifurcation of an $(n+1)$ -Neuron Bidirectional Associative Memory Neural Network Model With Delays

Publication Year: 2013, Page(s):118 - 132
Cited by:  Papers (29)
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Recent studies on Hopf bifurcations of neural networks with delays are confined to simplified neural network models consisting of only two, three, four, five, or six neurons. It is well known that neural networks are complex and large-scale nonlinear dynamical systems, so the dynamics of the delayed neural networks are very rich and complicated. Although discussing the dynamics of networks with a ... View full abstract»

• ### Prime Discriminant Simplicial Complex

Publication Year: 2013, Page(s):133 - 144
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The structure representation of data distribution plays an important role in understanding the underlying mechanism of generating data. In this paper, we propose the prime discriminant simplicial complex (PDSC) by utilizing persistent homology to capture such structures. Assuming that each class is represented with a prime simplicial complex, we classify unlabeled samples based on the nearest proj... View full abstract»

• ### Finite-Horizon Control-Constrained Nonlinear Optimal Control Using Single Network Adaptive Critics

Publication Year: 2013, Page(s):145 - 157
Cited by:  Papers (87)
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To synthesize fixed-final-time control-constrained optimal controllers for discrete-time nonlinear control-affine systems, a single neural network (NN)-based controller called the Finite-horizon Single Network Adaptive Critic is developed in this paper. Inputs to the NN are the current system states and the time-to-go, and the network outputs are the costates that are used to compute optimal feedb... View full abstract»

• ### Feature Combiners With Gate-Generated Weights for Classification

Publication Year: 2013, Page(s):158 - 163
Cited by:  Papers (7)
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Using functional weights in a conventional linear combination architecture is a way of obtaining expressive power and represents an alternative to classical trainable and implicit nonlinear transformations. In this brief, we explore this way of constructing binary classifiers, taking advantage of the possibility of generating functional weights by means of a gate with fixed radial basis functions.... View full abstract»

• ### Competitive Learning With Pairwise Constraints

Publication Year: 2013, Page(s):164 - 169
Cited by:  Papers (2)
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Constrained clustering has been an active research topic since the last decade. Most studies focus on batch-mode algorithms. This brief introduces two algorithms for on-line constrained learning, named on-line linear constrained vector quantization error (O-LCVQE) and constrained rival penalized competitive learning (C-RPCL). The former is a variant of the LCVQE algorithm for on-line settings, whe... View full abstract»

• ### Infinite Hidden Conditional Random Fields for Human Behavior Analysis

Publication Year: 2013, Page(s):170 - 177
Cited by:  Papers (17)
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Hidden conditional random fields (HCRFs) are discriminative latent variable models that have been shown to successfully learn the hidden structure of a given classification problem (provided an appropriate validation of the number of hidden states). In this brief, we present the infinite HCRF (iHCRF), which is a nonparametric model based on hierarchical Dirichlet processes and is capable of automa... View full abstract»

• ### 2014 IEEE World Congress on Computational Intelligence

Publication Year: 2013, Page(s): 178
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• ### Open Access

Publication Year: 2013, Page(s): 179
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Publication Year: 2013, Page(s): 180
| PDF (188 KB)
• ### IEEE Computational Intelligence Society Information

Publication Year: 2013, Page(s): C3
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• ### IEEE Transactions on Neural Networks information for authors

Publication Year: 2013, Page(s): C4
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## Aims & Scope

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.

Full Aims & Scope

## Meet Our Editors

Editor-in-Chief
Haibo He
Dept. of Electrical, Computer, and Biomedical Engineering
University of Rhode Island
Kingston, RI 02881, USA
ieeetnnls@gmail.com