Volume 25 Issue 12 • Dec. 2014
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Table of contents
Publication Year: 2014, Page(s): C1|
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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS publication information
Publication Year: 2014, Page(s): C2|
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Adaptive Neural Control for a Class of Nonlinear Time-Varying Delay Systems With Unknown Hysteresis
Publication Year: 2014, Page(s):2129 - 2140
Cited by: Papers (36)This paper investigates the fusion of unknown direction hysteresis model with adaptive neural control techniques in face of time-delayed continuous time nonlinear systems without strict-feedback form. Compared with previous works on the hysteresis phenomenon, the direction of the modified Bouc-Wen hysteresis model investigated in the literature is unknown. To reduce the computation burden in adapt... View full abstract»
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Optimal Control for Unknown Discrete-Time Nonlinear Markov Jump Systems Using Adaptive Dynamic Programming
Publication Year: 2014, Page(s):2141 - 2155
Cited by: Papers (46)In this paper, we develop and analyze an optimal control method for a class of discrete-time nonlinear Markov jump systems (MJSs) with unknown system dynamics. Specifically, an identifier is established for the unknown systems to approximate system states, and an optimal control approach for nonlinear MJSs is developed to solve the Hamilton-Jacobi-Bellman equation based on the adaptive dynamic pro... View full abstract»
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A Novel Estimation Algorithm Based on Data and Low-Order Models for Virtual Unmodeled Dynamics
Publication Year: 2014, Page(s):2156 - 2166
Cited by: Papers (2)In this paper, the challenging issue of estimating virtual unmodeled dynamics is addressed. A novel estimation algorithm based on historical data and the output of low-order approximation models for virtual un-modeled dynamics is presented. In particular, the virtual un-modeled dynamics are decomposed into known and unknown parts, where only the unknown part is to be estimated. The method effectiv... View full abstract»
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Structure-Constrained Low-Rank Representation
Publication Year: 2014, Page(s):2167 - 2179
Cited by: Papers (32)Benefiting from its effectiveness in subspace segmentation, low-rank representation (LRR) and its variations have many applications in computer vision and pattern recognition, such as motion segmentation, image segmentation, saliency detection, and semisupervised learning. It is known that the standard LRR can only work well under the assumption that all the subspaces are independent. However, thi... View full abstract»
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Exponential Stabilization for Sampled-Data Neural-Network-Based Control Systems
Publication Year: 2014, Page(s):2180 - 2190
Cited by: Papers (21)This paper investigates the problem of sampled-data stabilization for neural-network-based control systems with an optimal guaranteed cost. Using time-dependent Lyapunov functional approach, some novel conditions are proposed to guarantee the closed-loop systems exponentially stable, which fully use the available information about the actual sampling pattern. Based on the derived conditions, the d... View full abstract»
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Learning Regularized LDA by Clustering
Publication Year: 2014, Page(s):2191 - 2201
Cited by: Papers (42)As a supervised dimensionality reduction technique, linear discriminant analysis has a serious overfitting problem when the number of training samples per class is small. The main reason is that the between- and within-class scatter matrices computed from the limited number of training samples deviate greatly from the underlying ones. To overcome the problem without increasing the number of traini... View full abstract»
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A Deep Connection Between the Vapnik–Chervonenkis Entropy and the Rademacher Complexity
Publication Year: 2014, Page(s):2202 - 2211
Cited by: Papers (4)In this paper, we derive a deep connection between the Vapnik-Chervonenkis (VC) entropy and the Rademacher complexity. For this purpose, we first refine some previously known relationships between the two notions of complexity and then derive new results, which allow computing an admissible range for the Rademacher complexity, given a value of the VC-entropy, and vice versa. The approach adopted i... View full abstract»
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Learning Deep Hierarchical Visual Feature Coding
Publication Year: 2014, Page(s):2212 - 2225
Cited by: Papers (24)In this paper, we propose a hybrid architecture that combines the image modeling strengths of the bag of words framework with the representational power and adaptability of learning deep architectures. Local gradient-based descriptors, such as SIFT, are encoded via a hierarchical coding scheme composed of spatial aggregating restricted Boltzmann machines (RBM). For each coding layer, we regularize... View full abstract»
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A Parsimonious Mixture of Gaussian Trees Model for Oversampling in Imbalanced and Multimodal Time-Series Classification
Publication Year: 2014, Page(s):2226 - 2239
Cited by: Papers (2)We propose a novel framework of using a parsimonious statistical model, known as mixture of Gaussian trees, for modeling the possibly multimodal minority class to solve the problem of imbalanced time-series classification. By exploiting the fact that close-by time points are highly correlated due to smoothness of the time-series, our model significantly reduces the number of covariance parameters ... View full abstract»
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Semi-supervised Domain Adaptation on Manifolds
Publication Year: 2014, Page(s):2240 - 2249
Cited by: Papers (7)In real-life problems, the following semi-supervised domain adaptation scenario is often encountered: we have full access to some source data, which is usually very large; the target data distribution is under certain unknown transformation of the source data distribution; meanwhile, only a small fraction of the target instances come with labels. The goal is to learn a prediction model by incorpor... View full abstract»
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Real-Time Gesture Interface Based on Event-Driven Processing From Stereo Silicon Retinas
Publication Year: 2014, Page(s):2250 - 2263
Cited by: Papers (13)We propose a real-time hand gesture interface based on combining a stereo pair of biologically inspired event-based dynamic vision sensor (DVS) silicon retinas with neuromorphic event-driven postprocessing. Compared with conventional vision or 3-D sensors, the use of DVSs, which output asynchronous and sparse events in response to motion, eliminates the need to extract movements from sequences of ... View full abstract»
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Adaptive Neural PD Control With Semiglobal Asymptotic Stabilization Guarantee
Publication Year: 2014, Page(s):2264 - 2274
Cited by: Papers (25)This paper proves that adaptive neural plus proportional-derivative (PD) control can lead to semiglobal asymptotic stabilization rather than uniform ultimate boundedness for a class of uncertain affine nonlinear systems. An integral Lyapunov function-based ideal control law is introduced to avoid the control singularity problem. A variable-gain PD control term without the knowledge of plant bounds... View full abstract»
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Mandatory Leaf Node Prediction in Hierarchical Multilabel Classification
Publication Year: 2014, Page(s):2275 - 2287
Cited by: Papers (4)In hierarchical classification, the output labels reside on a tree- or directed acyclic graph (DAG)-structured hierarchy. On testing, the prediction paths of a given test example may be required to end at leaf nodes of the label hierarchy. This is called mandatory leaf node prediction (MLNP) and is particularly useful, when the leaf nodes have much stronger semantic meaning than the internal nodes... View full abstract»
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Synchronization in an Array of Output-Coupled Boolean Networks With Time Delay
Publication Year: 2014, Page(s):2288 - 2294
Cited by: Papers (78)This brief presents an analytical study of synchronization in an array of coupled deterministic Boolean networks (BNs) with time delay. Two kinds of models are considered. In one model, the outputs contain time delay, while in another one, the outputs do not. One restriction in this brief is that the state delay and output delay are restricted to be equal. By referring to the algebraic representat... View full abstract»
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Hybrid Manifold Embedding
Publication Year: 2014, Page(s):2295 - 2302
Cited by: Papers (6)In this brief, we present a novel supervised manifold learning framework dubbed hybrid manifold embedding (HyME). Unlike most of the existing supervised manifold learning algorithms that give linear explicit mapping functions, the HyME aims to provide a more general nonlinear explicit mapping function by performing a two-layer learning procedure. In the first layer, a new clustering strategy calle... View full abstract»
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Learning Deep and Wide: A Spectral Method for Learning Deep Networks
Publication Year: 2014, Page(s):2303 - 2308
Cited by: Papers (48)Building intelligent systems that are capable of extracting high-level representations from high-dimensional sensory data lies at the core of solving many computer vision-related tasks. We propose the multispectral neural networks (MSNN) to learn features from multicolumn deep neural networks and embed the penultimate hierarchical discriminative manifolds into a compact representation. The low-dim... View full abstract»
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On the Additive Properties of the Fat-Shattering Dimension
Publication Year: 2014, Page(s):2309 - 2312The properties of the VC-dimension under various compositions are well-understood, but this is much less the case for classes of continuous functions. In this brief, we show that a commonly used scale-sensitive dimension, Vy, is much less well-behaved under Minkowski summation than its VC cousin, while the fat-shattering dimension retains some compositional similarity to the VC-dimensio... View full abstract»
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2014 Index IEEE Transactions on Neural Networks and Learning Systems Vol. 25
Publication Year: 2014, Page(s):2313 - 2339|
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IEEE Computational Intelligence Society Information
Publication Year: 2014, Page(s): C3|
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IEEE Transactions on Neural Networks information for authors
Publication Year: 2014, 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.
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