# IEEE Transactions on Neural Networks and Learning Systems

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Displaying Results 1 - 23 of 23

Publication Year: 2017, Page(s): C1
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• ### IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS publication information

Publication Year: 2017, Page(s): C2
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• ### A Semisupervised Approach to the Detection and Characterization of Outliers in Categorical Data

Publication Year: 2017, Page(s):1017 - 1029
Cited by:  Papers (1)
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In this paper, we introduce a new approach of semisupervised anomaly detection that deals with categorical data. Given a training set of instances (all belonging to the normal class), we analyze the relationship among features for the extraction of a discriminative characterization of the anomalous instances. Our key idea is to build a model that characterizes the features of the normal instances ... View full abstract»

• ### High-Order Measurements for Residual Classifiers

Publication Year: 2017, Page(s):1030 - 1042
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Residual classifiers are common in dictionary-based multiclass classification. This paper proposes the concept of performance functions for residual classifiers. A performance function for multiclass classifications is a conceptual measurement function that combines local and global measurements. In general, the performance function is nonlinear. To explore the properties of the performance functi... View full abstract»

• ### High-Performance Consensus Control in Networked Systems With Limited Bandwidth Communication and Time-Varying Directed Topologies

Publication Year: 2017, Page(s):1043 - 1054
Cited by:  Papers (17)
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Communication data rates and energy constraints are two important factors that have to be considered in the coordination control of multiagent networks. Although some encoder-decoder-based consensus protocols are available, there still exists a fundamental theoretical problem: how can we further reduce the update rate of control input for each agent without the changing consensus performance? In t... View full abstract»

• ### Pinning Impulsive Synchronization of Reaction–Diffusion Neural Networks With Time-Varying Delays

Publication Year: 2017, Page(s):1055 - 1067
Cited by:  Papers (4)
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This paper investigates the exponential synchronization of reaction-diffusion neural networks with time-varying delays subject to Dirichlet boundary conditions. A novel type of pinning impulsive controllers is proposed to synchronize the reaction-diffusion neural networks with time-varying delays. By applying the Lyapunov functional method, sufficient verifiable conditions are constructed for the ... View full abstract»

• ### Robust Recurrent Kernel Online Learning

Publication Year: 2017, Page(s):1068 - 1081
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We propose a robust recurrent kernel online learning (RRKOL) algorithm based on the celebrated real-time recurrent learning approach that exploits the kernel trick in a recurrent online training manner. The novel RRKOL algorithm guarantees weight convergence with regularized risk management through the use of adaptive recurrent hyperparameters for superior generalization performance. Based on a ne... View full abstract»

• ### Learning Kernel Extended Dictionary for Face Recognition

Publication Year: 2017, Page(s):1082 - 1094
Cited by:  Papers (4)
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A sparse representation classifier (SRC) and a kernel discriminant analysis (KDA) are two successful methods for face recognition. An SRC is good at dealing with occlusion, while a KDA does well in suppressing intraclass variations. In this paper, we propose kernel extended dictionary (KED) for face recognition, which provides an efficient way for combining KDA and SRC. We first learn several kern... View full abstract»

• ### Learning Discriminative Subspaces on Random Contrasts for Image Saliency Analysis

Publication Year: 2017, Page(s):1095 - 1108
Cited by:  Papers (2)
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In visual saliency estimation, one of the most challenging tasks is to distinguish targets and distractors that share certain visual attributes. With the observation that such targets and distractors can sometimes be easily separated when projected to specific subspaces, we propose to estimate image saliency by learning a set of discriminative subspaces that perform the best in popping out targets... View full abstract»

• ### Coarse-to-Fine Learning for Single-Image Super-Resolution

Publication Year: 2017, Page(s):1109 - 1122
Cited by:  Papers (1)
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This paper develops a coarse-to-fine framework for single-image super-resolution (SR) reconstruction. The coarse-to-fine approach achieves high-quality SR recovery based on the complementary properties of both example learning-and reconstruction-based algorithms: example learning-based SR approaches are useful for generating plausible details from external exemplars but poor at suppressing aliasin... View full abstract»

• ### Affinity and Penalty Jointly Constrained Spectral Clustering With All-Compatibility, Flexibility, and Robustness

Publication Year: 2017, Page(s):1123 - 1138
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The existing, semisupervised, spectral clustering approaches have two major drawbacks, i.e., either they cannot cope with multiple categories of supervision or they sometimes exhibit unstable effectiveness. To address these issues, two normalized affinity and penalty jointly constrained spectral clustering frameworks as well as their corresponding algorithms, referred to as type-I affinity and pen... View full abstract»

• ### State Estimation for Discrete-Time Dynamical Networks With Time-Varying Delays and Stochastic Disturbances Under the Round-Robin Protocol

Publication Year: 2017, Page(s):1139 - 1151
Cited by:  Papers (7)
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This paper is concerned with the state estimation problem for a class of nonlinear dynamical networks with time-varying delays subject to the round-robin protocol. The communication between the state estimator and the nodes of the dynamical networks is implemented through a shared constrained network, in which only one node is allowed to send data at each time instant. The round-robin protocol is ... View full abstract»

• ### Event-Triggered State Estimation for Discrete-Time Multidelayed Neural Networks With Stochastic Parameters and Incomplete Measurements

Publication Year: 2017, Page(s):1152 - 1163
Cited by:  Papers (15)
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In this paper, the event-triggered state estimation problem is investigated for a class of discrete-time multidelayed neural networks with stochastic parameters and incomplete measurements. In order to cater for more realistic transmission process of the neural signals, we make the first attempt to introduce a set of stochastic variables to characterize the random fluctuations of system parameters... View full abstract»

• ### On Deep Learning for Trust-Aware Recommendations in Social Networks

Publication Year: 2017, Page(s):1164 - 1177
Cited by:  Papers (2)
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With the emergence of online social networks, the social network-based recommendation approach is popularly used. The major benefit of this approach is the ability of dealing with the problems with cold-start users. In addition to social networks, user trust information also plays an important role to obtain reliable recommendations. Although matrix factorization (MF) becomes dominant in recommend... View full abstract»

• ### A Note on the Unification of Adaptive Online Learning

Publication Year: 2017, Page(s):1178 - 1191
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In online convex optimization, adaptive algorithms, which can utilize the second-order information of the loss function's (sub)gradient, have shown improvements over standard gradient methods. This paper presents a framework Follow the Bregman Divergence Leader that unifies various existing adaptive algorithms from which new insights are revealed. Under the proposed framework, two simple adaptive ... View full abstract»

• ### LIF and Simplified SRM Neurons Encode Signals Into Spikes via a Form of Asynchronous Pulse Sigma–Delta Modulation

Publication Year: 2017, Page(s):1192 - 1205
Cited by:  Papers (1)
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We show how two spiking neuron models encode continuous-time signals into spikes (action potentials, time-encoded pulses, or point processes) using a special form of sigma-delta modulation (SDM). In particular, we show that the well-known leaky integrate-and-fire (LIF) neuron and the simplified spike response model (SRM0) neuron encode the continuous-time signals into spikes via a proposed asynchr... View full abstract»

• ### A Collective Neurodynamic Approach to Constrained Global Optimization

Publication Year: 2017, Page(s):1206 - 1215
Cited by:  Papers (3)
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Global optimization is a long-lasting research topic in the field of optimization, posting many challenging theoretic and computational issues. This paper presents a novel collective neurodynamic method for solving constrained global optimization problems. At first, a one-layer recurrent neural network (RNN) is presented for searching the Karush-Kuhn-Tucker points of the optimization problem under... View full abstract»

• ### Adaptive Neural Network Nonparametric Identifier With Normalized Learning Laws

Publication Year: 2017, Page(s):1216 - 1227
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This paper addresses the design of a normalized convergent learning law for neural networks (NNs) with continuous dynamics. The NN is used here to obtain a nonparametric model for uncertain systems described by a set of ordinary differential equations. The source of uncertainties is the presence of some external perturbations and poor knowledge of the nonlinear function describing the system dynam... View full abstract»

• ### Speeding Up Cellular Neural Network Processing Ability by Embodying Memristors

Publication Year: 2017, Page(s):1228 - 1232
Cited by:  Papers (3)
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Cellular neural networks (CNNs) are an efficient tool for image analysis and pattern recognition. Based on elementary cells connected to neighboring units, they are easy to install in hardware, carrying out massively parallel processes. This brief presents a new model of CNN with memory devices, which enhances further CNN performance. By introducing a memristive element in basic cells, we carry ou... View full abstract»

• ### Mixtures of Conditional Random Fields for Improved Structured Output Prediction

Publication Year: 2017, Page(s):1233 - 1240
Cited by:  Papers (1)
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The conditional random field (CRF) is a successful probabilistic model for structured output prediction problems. In this brief, we consider to enlarge the representational capacity of CRF via mixture modeling. The motivation is that a single CRF can perform well if the data conform to the statistical dependence assumption imposed by the CRF model structure, whereas it may potentially fail to mode... View full abstract»

• ### A Robust Regularization Path Algorithm for $nu$ -Support Vector Classification

Publication Year: 2017, Page(s):1241 - 1248
Cited by:  Papers (149)
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The v-support vector classification has the advantage of using a regularization parameter v to control the number of support vectors and margin errors. Recently, a regularization path algorithm for v-support vector classification (v-SvcPath) suffers exceptions and singularities in some special cases. In this brief, we first present a new equivalent dual formulation for v-SVC and, then, propose a r... View full abstract»

• ### IEEE Computational Intelligence Society Information

Publication Year: 2017, Page(s): C3
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• ### IEEE Transactions on Neural Networks information for authors

Publication Year: 2017, 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