IEEE Transactions on Neural Networks and Learning Systems

Issue 12 • Dec. 2013

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  • 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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  • Canonical Correlation Analysis on Data With Censoring and Error Information

    Publication Year: 2013, Page(s):1909 - 1919
    Cited by:  Papers (4)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (763 KB) | HTML iconHTML

    We developed a probabilistic model for canonical correlation analysis in the case when the associated datasets are incomplete. This case can arise where data entries either contain measurement errors or are censored (i.e., nonignorable missing) due to uncertainties in instrument calibration and physical limitations of devices and experimental conditions. The aim of our model is to estimate the tru... View full abstract»

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  • Highly Accurate Moving Object Detection in Variable Bit Rate Video-Based Traffic Monitoring Systems

    Publication Year: 2013, Page(s):1920 - 1931
    Cited by:  Papers (30)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (3010 KB) | HTML iconHTML

    Automated motion detection, which segments moving objects from video streams, is the key technology of intelligent transportation systems for traffic management. Traffic surveillance systems use video communication over real-world networks with limited bandwidth, which frequently suffers because of either network congestion or unstable bandwidth. Evidence supporting these problems abounds in publi... View full abstract»

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  • Recurrent Neural Collective Classification

    Publication Year: 2013, Page(s):1932 - 1943
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    With the recent surge in availability of data sets containing not only individual attributes but also relationships, classification techniques that take advantage of predictive relationship information have gained in popularity. The most popular existing collective classification techniques have a number of limitations-some of them generate arbitrary and potentially lossy summaries of the relation... View full abstract»

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  • Online Selective Kernel-Based Temporal Difference Learning

    Publication Year: 2013, Page(s):1944 - 1956
    Cited by:  Papers (10)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2039 KB) | HTML iconHTML

    In this paper, an online selective kernel-based temporal difference (OSKTD) learning algorithm is proposed to deal with large scale and/or continuous reinforcement learning problems. OSKTD includes two online procedures: online sparsification and parameter updating for the selective kernel-based value function. A new sparsification method (i.e., a kernel distance-based online sparsification method... View full abstract»

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  • Stability and Synchronization of Discrete-Time Neural Networks With Switching Parameters and Time-Varying Delays

    Publication Year: 2013, Page(s):1957 - 1972
    Cited by:  Papers (51)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (803 KB) | HTML iconHTML

    This paper is concerned with the problems of exponential stability analysis and synchronization of discrete-time switched delayed neural networks. Using the average dwell time approach together with the piecewise Lyapunov function technique, sufficient conditions are proposed to guarantee the exponential stability for the switched neural networks with time-delays. Benefitting from the delay partit... View full abstract»

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  • Artificial Endocrine Controller for Power Management in Robotic Systems

    Publication Year: 2013, Page(s):1973 - 1985
    Cited by:  Papers (5)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1607 KB) | HTML iconHTML

    The robots that operate autonomously for extended periods in remote environments are often limited to gather only small amounts of power through photovoltaic solar panels. Such limited power budgets make power management critical to the success of the robot's mission. Artificial endocrine controllers, inspired by the mammalian endocrine system, have shown potential as a method for managing competi... View full abstract»

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  • Operator Control of Interneural Computing Machines

    Publication Year: 2013, Page(s):1986 - 1998
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1801 KB) | HTML iconHTML

    A dynamic representation of neural population responses asserts that motor cortex is a flexible pattern generator sending rhythmic, oscillatory signals to generate multiphasic patterns of movement. This raises a question concerning the design and control of new computing machines that mimic the oscillatory patterns and multiphasic patterns seen in neural systems. To address this issue, we design a... View full abstract»

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  • Multiple Graph Label Propagation by Sparse Integration

    Publication Year: 2013, Page(s):1999 - 2012
    Cited by:  Papers (13)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (3490 KB) | HTML iconHTML

    Graph-based approaches have been most successful in semisupervised learning. In this paper, we focus on label propagation in graph-based semisupervised learning. One essential point of label propagation is that the performance is heavily affected by incorporating underlying manifold of given data into the input graph. The other more important point is that in many recent real-world applications, t... View full abstract»

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  • Universal Blind Image Quality Assessment Metrics Via Natural Scene Statistics and Multiple Kernel Learning

    Publication Year: 2013, Page(s):2013 - 2026
    Cited by:  Papers (39)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2844 KB) | HTML iconHTML

    Universal blind image quality assessment (IQA) metrics that can work for various distortions are of great importance for image processing systems, because neither ground truths are available nor the distortion types are aware all the time in practice. Existing state-of-the-art universal blind IQA algorithms are developed based on natural scene statistics (NSS). Although NSS-based metrics obtained ... View full abstract»

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  • $H_{infty}$ State Estimation for Complex Networks With Uncertain Inner Coupling and Incomplete Measurements

    Publication Year: 2013, Page(s):2027 - 2037
    Cited by:  Papers (90)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (617 KB) | HTML iconHTML

    In this paper, the H∞ state estimation problem is investigated for a class of complex networks with uncertain coupling strength and incomplete measurements. With the aid of the interval matrix approach, we make the first attempt to characterize the uncertainties entering into the inner coupling matrix. The incomplete measurements under consideration include sensor saturations, qu... View full abstract»

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  • Goal Representation Heuristic Dynamic Programming on Maze Navigation

    Publication Year: 2013, Page(s):2038 - 2050
    Cited by:  Papers (34)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2423 KB) | HTML iconHTML

    Goal representation heuristic dynamic programming (GrHDP) is proposed in this paper to demonstrate online learning in the Markov decision process. In addition to the (external) reinforcement signal in literature, we develop an adaptively internal goal/reward representation for the agent with the proposed goal network. Specifically, we keep the actor-critic design in heuristic dynamic programming (... View full abstract»

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  • Accelerated Canonical Polyadic Decomposition Using Mode Reduction

    Publication Year: 2013, Page(s):2051 - 2062
    Cited by:  Papers (6)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1151 KB) | HTML iconHTML

    CANonical polyadic DECOMPosition (CANDECOMP, CPD), also known as PARAllel FACtor analysis (PARAFAC) is widely applied to Nth-order (N ≥ 3) tensor analysis. Existing CPD methods mainly use alternating least squares iterations and hence need to unfold tensors to each of their N modes frequently, which is one major performance bottleneck for large-scale data, especially when the order N is lar... View full abstract»

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  • Hardware Friendly Probabilistic Spiking Neural Network With Long-Term and Short-Term Plasticity

    Publication Year: 2013, Page(s):2063 - 2074
    Cited by:  Papers (7)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1680 KB) | HTML iconHTML

    This paper proposes a probabilistic spiking neural network (PSNN) with unimodal weight distribution, possessing long- and short-term plasticity. The proposed algorithm is derived by both the arithmetic gradient decent calculation and bioinspired algorithms. The algorithm is benchmarked by the Iris and Wisconsin breast cancer (WBC) data sets. The network features fast convergence speed and high acc... View full abstract»

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  • Neural Network Architecture for Cognitive Navigation in Dynamic Environments

    Publication Year: 2013, Page(s):2075 - 2087
    Cited by:  Papers (10)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2169 KB) | HTML iconHTML

    Navigation in time-evolving environments with moving targets and obstacles requires cognitive abilities widely demonstrated by even simplest animals. However, it is a long-standing challenging problem for artificial agents. Cognitive autonomous robots coping with this problem must solve two essential tasks: 1) understand the environment in terms of what may happen and how I can deal with this and ... View full abstract»

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  • An Equivalence Between Adaptive Dynamic Programming With a Critic and Backpropagation Through Time

    Publication Year: 2013, Page(s):2088 - 2100
    Cited by:  Papers (14)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (592 KB) | HTML iconHTML

    We consider the adaptive dynamic programming technique called Dual Heuristic Programming (DHP), which is designed to learn a critic function, when using learned model functions of the environment. DHP is designed for optimizing control problems in large and continuous state spaces. We extend DHP into a new algorithm that we call Value-Gradient Learning, VGL(λ), and prove equivalence of an i... View full abstract»

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  • Semisupervised Multitask Learning With Gaussian Processes

    Publication Year: 2013, Page(s):2101 - 2112
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1376 KB) | HTML iconHTML

    We present a probabilistic framework for transferring learning across tasks and between labeled and unlabeled data. The approach is based on Gaussian process (GP) prediction and incorporates both the geometry of the data and the similarity between tasks within a GP covariance, allowing Bayesian prediction in a natural way. We discuss the transfer of learning in a multitask scenario in the two case... View full abstract»

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  • Nonlinear Projection Trick in Kernel Methods: An Alternative to the Kernel Trick

    Publication Year: 2013, Page(s):2113 - 2119
    Cited by:  Papers (6)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (237 KB) | HTML iconHTML

    In kernel methods such as kernel principal component analysis (PCA) and support vector machines, the so called kernel trick is used to avoid direct calculations in a high (virtually infinite) dimensional kernel space. In this brief, based on the fact that the effective dimensionality of a kernel space is less than the number of training samples, we propose an alternative to the kernel trick that e... View full abstract»

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  • 2014 IEEE World Congress on Computational Intelligence

    Publication Year: 2013, Page(s): 2120
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  • 2013 Index IEEE Transactions on Neural Networks and Learning Systems Vol. 24

    Publication Year: 2013, Page(s):2121 - 2146
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  • IEEE Transactions on Neural Networks information for authors

    Publication Year: 2013, Page(s): C4
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  • IEEE Computational Intelligence Society Information

    Publication Year: 2013, Page(s): C3
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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