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IEEE Transactions on Neural Networks and Learning Systems

Issue 6 • June 2013

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Displaying Results 1 - 19 of 19
  • Table of contents

    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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  • Algorithmic Survey of Parametric Value Function Approximation

    Publication Year: 2013, Page(s):845 - 867
    Cited by:  Papers (11)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (524 KB) | HTML iconHTML

    Reinforcement learning (RL) is a machine learning answer to the optimal control problem. It consists of learning an optimal control policy through interactions with the system to be controlled, the quality of this policy being quantified by the so-called value function. A recurrent subtopic of RL concerns computing an approximation of this value function when the system is too large for an exact r... View full abstract»

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  • Impulsive Control for Existence, Uniqueness, and Global Stability of Periodic Solutions of Recurrent Neural Networks With Discrete and Continuously Distributed Delays

    Publication Year: 2013, Page(s):868 - 877
    Cited by:  Papers (44)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2077 KB) | HTML iconHTML

    In this paper, a class of recurrent neural networks with discrete and continuously distributed delays is considered. Sufficient conditions for the existence, uniqueness, and global exponential stability of a periodic solution are obtained by using contraction mapping theorem and stability theory on impulsive functional differential equations. The proposed method, which differs from the existing re... View full abstract»

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  • Effective Neural Network Ensemble Approach for Improving Generalization Performance

    Publication Year: 2013, Page(s):878 - 887
    Cited by:  Papers (14)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (429 KB) | HTML iconHTML

    This paper, with an aim at improving neural networks' generalization performance, proposes an effective neural network ensemble approach with two novel ideas. One is to apply neural networks' output sensitivity as a measure to evaluate neural networks' output diversity at the inputs near training samples so as to be able to select diverse individuals from a pool of well-trained neural networks; th... View full abstract»

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  • Novel Cost-Sensitive Approach to Improve the Multilayer Perceptron Performance on Imbalanced Data

    Publication Year: 2013, Page(s):888 - 899
    Cited by:  Papers (21)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (596 KB) | HTML iconHTML

    Traditional learning algorithms applied to complex and highly imbalanced training sets may not give satisfactory results when distinguishing between examples of the classes. The tendency is to yield classification models that are biased towards the overrepresented (majority) class. This paper investigates this class imbalance problem in the context of multilayer perceptron (MLP) neural networks. T... View full abstract»

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  • Robust Kernel Representation With Statistical Local Features for Face Recognition

    Publication Year: 2013, Page(s):900 - 912
    Cited by:  Papers (31)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (668 KB) | HTML iconHTML

    Factors such as misalignment, pose variation, and occlusion make robust face recognition a difficult problem. It is known that statistical features such as local binary pattern are effective for local feature extraction, whereas the recently proposed sparse or collaborative representation-based classification has shown interesting results in robust face recognition. In this paper, we propose a nov... View full abstract»

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  • Adaptive Learning in Tracking Control Based on the Dual Critic Network Design

    Publication Year: 2013, Page(s):913 - 928
    Cited by:  Papers (60)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2375 KB) | HTML iconHTML

    In this paper, we present a new adaptive dynamic programming approach by integrating a reference network that provides an internal goal representation to help the systems learning and optimization. Specifically, we build the reference network on top of the critic network to form a dual critic network design that contains the detailed internal goal representation to help approximate the value funct... View full abstract»

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  • Sparse Coding From a Bayesian Perspective

    Publication Year: 2013, Page(s):929 - 939
    Cited by:  Papers (21)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (969 KB) | HTML iconHTML

    Sparse coding is a promising theme in computer vision. Most of the existing sparse coding methods are based on either l0 or l1 penalty, which often leads to unstable solution or biased estimation. This is because of the nonconvexity and discontinuity of the l0 penalty and the over-penalization on the true large coefficients of the l... View full abstract»

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  • FSMRank: Feature Selection Algorithm for Learning to Rank

    Publication Year: 2013, Page(s):940 - 952
    Cited by:  Papers (8)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (525 KB) | HTML iconHTML

    In recent years, there has been growing interest in learning to rank. The introduction of feature selection into different learning problems has been proven effective. These facts motivate us to investigate the problem of feature selection for learning to rank. We propose a joint convex optimization formulation which minimizes ranking errors while simultaneously conducting feature selection. This ... View full abstract»

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  • Fractional Norm Regularization: Learning With Very Few Relevant Features

    Publication Year: 2013, Page(s):953 - 963
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (584 KB) | HTML iconHTML

    Learning in the presence of a large number of irrelevant features is an important problem in high-dimensional tasks. Previous studies have shown that L1-norm regularization can be effective in such cases while L2-norm regularization is not. Furthermore, work in compressed sensing suggests that regularization by nonconvex (e.g., fractional) semi-norms may outperform L1-regularization. However, for ... View full abstract»

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  • Exponential Family Factors for Bayesian Factor Analysis

    Publication Year: 2013, Page(s):964 - 976
    Cited by:  Papers (8)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1154 KB) | HTML iconHTML

    Expressing data as linear functions of a small number of unknown variables is a useful approach employed by several classical data analysis methods, e.g., factor analysis, principal component analysis, or latent semantic indexing. These models represent the data using the product of two factors. In practice, one important concern is how to link the learned factors to relevant quantities in the con... View full abstract»

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  • Backtrackless Walks on a Graph

    Publication Year: 2013, Page(s):977 - 989
    Cited by:  Papers (13)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (974 KB) | HTML iconHTML

    The aim of this paper is to explore the use of backtrackless walks and prime cycles for characterizing both labeled and unlabeled graphs. The reason for using backtrackless walks and prime cycles is that they avoid tottering, and can increase the discriminative power of the resulting graph representation. However, the use of such methods is limited in practice because of their computational cost. ... View full abstract»

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  • Fast-Convergent Double-Sigmoid Hopfield Neural Network as Applied to Optimization Problems

    Publication Year: 2013, Page(s):990 - 996
    Cited by:  Papers (7)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1400 KB) | HTML iconHTML

    The Hopfield neural network (HNN) has been widely used in numerous different optimization problems since the early 1980s. The convergence speed of the HNN (already in high gain) eventually plays a critical role in various real-time applications. In this brief, we propose and analyze a generalized HNN which drastically improves the convergence speed of the network, and thus allows benefiting from t... View full abstract»

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  • Synchronization Design of Boolean Networks Via the Semi-Tensor Product Method

    Publication Year: 2013, Page(s):996 - 1001
    Cited by:  Papers (10)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (263 KB) | HTML iconHTML

    We provide a general approach for the design of a response Boolean network (BN) to achieve complete synchronization with a given drive BN. The approach is based on the algebraic representation of BNs in terms of the semi-tensor product of matrices. Instead of designing the logical dynamic equations of a response BN directly, we first construct its algebraic representation and then convert the alge... View full abstract»

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  • Bogdanov–Takens Singularity in Tri-Neuron Network With Time Delay

    Publication Year: 2013, Page(s):1001 - 1007
    Cited by:  Papers (28)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (899 KB) | HTML iconHTML

    This brief reports a retarded functional differential equation modeling tri-neuron network with time delay. The Bogdanov-Takens (B-T) bifurcation is investigated by using the center manifold reduction and the normal form method. We get the versal unfolding of the norm forms at the B-T singularity and show that the model can exhibit pitchfork, Hopf, homoclinic, and double-limit cycles bifurcations.... View full abstract»

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  • 2014 IEEE World Congress on Computational Intelligence

    Publication Year: 2013, Page(s): 1008
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  • 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