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Neural Networks, IEEE Transactions on

Issue 1 • Date Jan. 2008

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Displaying Results 1 - 23 of 23
  • Table of contents

    Publication Year: 2008 , Page(s): C1 - C4
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  • IEEE Transactions on Neural Networks publication information

    Publication Year: 2008 , Page(s): C2
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  • Editorial: A New Era for the IEEE Transactions on Neural Networks

    Publication Year: 2008 , Page(s): 1 - 2
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  • A Dynamically Adjusted Mixed Emphasis Method for Building Boosting Ensembles

    Publication Year: 2008 , Page(s): 3 - 17
    Cited by:  Papers (6)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (736 KB) |  | HTML iconHTML  

    Progressively emphasizing samples that are difficult to classify correctly is the base for the recognized high performance of real Adaboost (RA) ensembles. The corresponding emphasis function can be written as a product of a factor that measures the quadratic error and a factor related to the proximity to the classification border; this fact opens the door to explore the potential advantages provi... View full abstract»

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  • MPCA: Multilinear Principal Component Analysis of Tensor Objects

    Publication Year: 2008 , Page(s): 18 - 39
    Cited by:  Papers (81)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (1667 KB) |  | HTML iconHTML  

    This paper introduces a multilinear principal component analysis (MPCA) framework for tensor object feature extraction. Objects of interest in many computer vision and pattern recognition applications, such as 2D/3D images and video sequences are naturally described as tensors or multilinear arrays. The proposed framework performs feature extraction by determining a multilinear projection that cap... View full abstract»

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  • Local Feature Weighting in Nearest Prototype Classification

    Publication Year: 2008 , Page(s): 40 - 53
    Cited by:  Papers (12)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (613 KB) |  | HTML iconHTML  

    The distance metric is the corner stone of nearest neighbor (NN)-based methods, and therefore, of nearest prototype (NP) algorithms. That is because they classify depending on the similarity of the data. When the data is characterized by a set of features which may contribute to the classification task in different levels, feature weighting or selection is required, sometimes in a local sense. How... View full abstract»

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  • Adaptive Fuzzy-Neural-Network Control for Maglev Transportation System

    Publication Year: 2008 , Page(s): 54 - 70
    Cited by:  Papers (15)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (1183 KB) |  | HTML iconHTML  

    A magnetic-levitation (maglev) transportation system including levitation and propulsion control is a subject of considerable scientific interest because of highly nonlinear and unstable behaviors. In this paper, the dynamic model of a maglev transportation system including levitated electromagnets and a propulsive linear induction motor (LIM) based on the concepts of mechanical geometry and motio... View full abstract»

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  • Impulsive Stabilization of High-Order Hopfield-Type Neural Networks With Time-Varying Delays

    Publication Year: 2008 , Page(s): 71 - 79
    Cited by:  Papers (21)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (407 KB) |  | HTML iconHTML  

    This paper studies the problems of global exponential stability for impulsive high-order Hopfield-type neural networks (NNs) with time-varying delays. By employing the Lyapunov-Razumikhin technique, some criteria ensuring global exponential stability are derived. Our results are then used to obtain some sufficient conditions under which some NNs can be forced to converge by impulsive control. Nume... View full abstract»

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  • Neural Network Adaptive Control for a Class of Nonlinear Uncertain Dynamical Systems With Asymptotic Stability Guarantees

    Publication Year: 2008 , Page(s): 80 - 89
    Cited by:  Papers (42)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (506 KB) |  | HTML iconHTML  

    In this paper, a neuroadaptive control framework for continuous- and discrete-time nonlinear uncertain dynamical systems with input-to-state stable internal dynamics is developed. The proposed framework is Lyapunov based and unlike standard neural network (NN) controllers guaranteeing ultimate boundedness, the framework guarantees partial asymptotic stability of the closed-loop system, that is, as... View full abstract»

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  • Generalized Hamilton–Jacobi–Bellman Formulation -Based Neural Network Control of Affine Nonlinear Discrete-Time Systems

    Publication Year: 2008 , Page(s): 90 - 106
    Cited by:  Papers (45)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (1281 KB) |  | HTML iconHTML  

    In this paper, we consider the use of nonlinear networks towards obtaining nearly optimal solutions to the control of nonlinear discrete-time (DT) systems. The method is based on least squares successive approximation solution of the generalized Hamilton-Jacobi-Bellman (GHJB) equation which appears in optimization problems. Successive approximation using the GHJB has not been applied for nonlinear... View full abstract»

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  • A Generalized Least Absolute Deviation Method for Parameter Estimation of Autoregressive Signals

    Publication Year: 2008 , Page(s): 107 - 118
    Cited by:  Papers (12)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (745 KB) |  | HTML iconHTML  

    This paper proposes a generalized least absolute deviation (GLAD) method for parameter estimation of autoregressive (AR) signals under non-Gaussian noise environments. The proposed GLAD method can improve the accuracy of the estimation of the conventional least absolute deviation (LAD) method by minimizing a new cost function with parameter variables and noise error variables. Compared with second... View full abstract»

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  • A New Jacobian Matrix for Optimal Learning of Single-Layer Neural Networks

    Publication Year: 2008 , Page(s): 119 - 129
    Cited by:  Papers (12)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (669 KB) |  | HTML iconHTML  

    This paper investigates the learning of a wide class of single-hidden-layer feedforward neural networks (SLFNs) with two sets of adjustable parameters, i.e., the nonlinear parameters in the hidden nodes and the linear output weights. The main objective is to both speed up the convergence of second-order learning algorithms such as Levenberg-Marquardt (LM), as well as to improve the network perform... View full abstract»

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  • Multiclass Posterior Probability Support Vector Machines

    Publication Year: 2008 , Page(s): 130 - 139
    Cited by:  Papers (20)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (1069 KB) |  | HTML iconHTML  

    Tao et. al. have recently proposed the posterior probability support vector machine (PPSVM) which uses soft labels derived from estimated posterior probabilities to be more robust to noise and outliers. Tao et. al.'s model uses a window-based density estimator to calculate the posterior probabilities and is a binary classifier. We propose a neighbor-based density estimator and also extend the mode... View full abstract»

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  • Convergence of Nonautonomous Cohen–Grossberg-Type Neural Networks With Variable Delays

    Publication Year: 2008 , Page(s): 140 - 147
    Cited by:  Papers (11)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (533 KB) |  | HTML iconHTML  

    This paper is concerned with the global convergence of the solutions of a nonautonomous system with variable delays, arising from the description of the states of neurons in delayed Cohen-Grossberg type in a time-varying situation. By exploring intrinsic features between nonautonomous system and its asymptotic equation, several novel sufficient conditions are established to ensure that all solutio... View full abstract»

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  • Pruning Noisy Bases in Discriminant Analysis

    Publication Year: 2008 , Page(s): 148 - 157
    Cited by:  Papers (10)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (918 KB) |  | HTML iconHTML  

    The success of linear discriminant analysis (LDA) is due in part to the simplicity of its formulation, which reduces to a simultaneous diagonalization of two symmetric matrices A and B. However, a fundamental drawback of this approach is that it cannot be efficiently applied wherever the matrix A is singular or when some of the smallest variances in are due to noise. In this paper, we present a fa... View full abstract»

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  • Multiperiodicity and Attractivity of Delayed Recurrent Neural Networks With Unsaturating Piecewise Linear Transfer Functions

    Publication Year: 2008 , Page(s): 158 - 167
    Cited by:  Papers (28)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (552 KB) |  | HTML iconHTML  

    This paper studies multiperiodicity and attractivity for a class of recurrent neural networks (RNNs) with unsaturating piecewise linear transfer functions and variable delays. Using local inhibition, conditions for boundedness and global attractivity are established. These conditions allow coexistence of stable and unstable trajectories. Moreover, multiperiodicity of the network is investigated by... View full abstract»

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  • Unsupervised Segmentation With Dynamical Units

    Publication Year: 2008 , Page(s): 168 - 182
    Cited by:  Papers (8)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (1495 KB) |  | HTML iconHTML  

    In this paper, we present a novel network to separate mixtures of inputs that have been previously learned. A significant capability of the network is that it segments the components of each input object that most contribute to its classification. The network consists of amplitude-phase units that can synchronize their dynamics, so that separation is determined by the amplitude of units in an outp... View full abstract»

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  • An Assessment of Qualitative Performance of Machine Learning Architectures: Modular Feedback Networks

    Publication Year: 2008 , Page(s): 183 - 189
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (2071 KB) |  | HTML iconHTML  

    A framework for the assessment of qualitative performance of machine learning architectures is proposed. For generality, the analysis is provided for the modular nonlinear pipelined recurrent neural network (PRNN) architecture. This is supported by a sensitivity analysis, which is achieved based upon the prediction performance with respect to changes in the nature of the processed signal and by ut... View full abstract»

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  • Recursive Support Vector Machines for Dimensionality Reduction

    Publication Year: 2008 , Page(s): 189 - 193
    Cited by:  Papers (22)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (187 KB) |  | HTML iconHTML  

    The usual dimensionality reduction technique in supervised learning is mainly based on linear discriminant analysis (LDA), but it suffers from singularity or undersampled problems. On the other hand, a regular support vector machine (SVM) separates the data only in terms of one single direction of maximum margin, and the classification accuracy may be not good enough. In this letter, a recursive S... View full abstract»

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  • A Forward-Constrained Regression Algorithm for Sparse Kernel Density Estimation

    Publication Year: 2008 , Page(s): 193 - 198
    Cited by:  Papers (11)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (266 KB) |  | HTML iconHTML  

    Using the classical Parzen window (PW) estimate as the target function, the sparse kernel density estimator is constructed in a forward-constrained regression (FCR) manner. The proposed algorithm selects significant kernels one at a time, while the leave-one-out (LOO) test score is minimized subject to a simple positivity constraint in each forward stage. The model parameter estimation in each for... View full abstract»

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  • Computational Auditory Scene Analysis: Principles, Algorithms, and Applications (Wang, D. and Brown, G.J., Eds.; 2006) [Book review]

    Publication Year: 2008 , Page(s): 199
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  • In this issue - Technically

    Publication Year: 2008 , Page(s): 200
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  • IEEE Computational Intelligence Society Information

    Publication Year: 2008 , Page(s): C3
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Aims & Scope

IEEE Transactions on Neural Networks is devoted to the science and technology of neural networks, which disclose significant technical knowledge, exploratory developments, and applications of neural networks from biology to software to hardware.

 

This Transactions ceased production in 2011. The current retitled publication is IEEE Transactions on Neural Networks and Learning Systems.

Full Aims & Scope