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IEEE Transactions on Neural Networks

Issue 2 • Date Feb. 2008

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  • 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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  • Preliminary Study on Wilcoxon Learning Machines

    Publication Year: 2008, Page(s):201 - 211
    Cited by:  Papers (36)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1224 KB) | HTML iconHTML

    As is well known in statistics, the resulting linear regressors by using the rank-based Wilcoxon approach to linear regression problems are usually robust against (or insensitive to) outliers. This motivates us to introduce in this paper the Wilcoxon approach to the area of machine learning. Specifically, we investigate four new learning machines, namely Wilcoxon neural network (WNN), Wilcoxon gen... View full abstract»

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  • A Hierarchical Graph Neuron Scheme for Real-Time Pattern Recognition

    Publication Year: 2008, Page(s):212 - 229
    Cited by:  Papers (33)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1739 KB) | HTML iconHTML

    The hierarchical graph neuron (HGN) implements a single cycle memorization and recall operation through a novel algorithmic design. The HGN is an improvement on the already published original graph neuron (GN) algorithm. In this improved approach, it recognizes incomplete/noisy patterns. It also resolves the crosstalk problem, which is identified in the previous publications, within closely matche... View full abstract»

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  • Integrating Temporal Difference Methods and Self-Organizing Neural Networks for Reinforcement Learning With Delayed Evaluative Feedback

    Publication Year: 2008, Page(s):230 - 244
    Cited by:  Papers (34)  |  Patents (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (815 KB) | HTML iconHTML

    This paper presents a neural architecture for learning category nodes encoding mappings across multimodal patterns involving sensory inputs, actions, and rewards. By integrating adaptive resonance theory (ART) and temporal difference (TD) methods, the proposed neural model, called TD fusion architecture for learning, cognition, and navigation (TD-FALCON), enables an autonomous agent to adapt and f... View full abstract»

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  • Ranked Centroid Projection: A Data Visualization Approach With Self-Organizing Maps

    Publication Year: 2008, Page(s):245 - 259
    Cited by:  Papers (10)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1888 KB) | HTML iconHTML

    The self-organizing map (SOM) is an efficient tool for visualizing high-dimensional data. In this paper, the clustering and visualization capabilities of the SOM, especially in the analysis of textual data, i.e., document collections, are reviewed and further developed. A novel clustering and visualization approach based on the SOM is proposed for the task of text mining. The proposed approach fir... View full abstract»

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  • Maxi–Min Margin Machine: Learning Large Margin Classifiers Locally and Globally

    Publication Year: 2008, Page(s):260 - 272
    Cited by:  Papers (36)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1217 KB) | HTML iconHTML

    In this paper, we propose a novel large margin classifier, called the maxi-min margin machine (M4). This model learns the decision boundary both locally and globally. In comparison, other large margin classifiers construct separating hyperplanes only either locally or globally. For example, a state-of-the-art large margin classifier, the support vector machine (SVM), considers data only... View full abstract»

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  • Constrained Least Absolute Deviation Neural Networks

    Publication Year: 2008, Page(s):273 - 283
    Cited by:  Papers (10)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (523 KB) | HTML iconHTML

    It is well known that least absolute deviation (LAD) criterion or -norm used for estimation of parameters is characterized by robustness, i.e., the estimated parameters are totally resistant (insensitive) to large changes in the sampled data. This is an extremely useful feature, especially, when the sampled data are known to be contaminated by occasionally occurring outliers or by spiky noise. In ... View full abstract»

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  • Best Approximation of Gaussian Neural Networks With Nodes Uniformly Spaced

    Publication Year: 2008, Page(s):284 - 298
    Cited by:  Papers (8)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (415 KB) | HTML iconHTML

    This paper is aimed at exposing the reader to certain aspects in the design of the best approximants with Gaussian radial basis functions (RBFs). The class of functions to which this approach applies consists of those compactly supported in frequency. The approximative properties of uniqueness and existence are restricted to this class. Functions which are smooth enough can be expanded in Gaussian... View full abstract»

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  • Recursive Neural Network Rule Extraction for Data With Mixed Attributes

    Publication Year: 2008, Page(s):299 - 307
    Cited by:  Papers (39)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (514 KB) | HTML iconHTML

    In this paper, we present a recursive algorithm for extracting classification rules from feedforward neural networks (NNs) that have been trained on data sets having both discrete and continuous attributes. The novelty of this algorithm lies in the conditions of the extracted rules: the rule conditions involving discrete attributes are disjoint from those involving continuous attributes. The algor... View full abstract»

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  • A Neural Network Method for Detection of Obstructive Sleep Apnea and Narcolepsy Based on Pupil Size and EEG

    Publication Year: 2008, Page(s):308 - 318
    Cited by:  Papers (30)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (848 KB) | HTML iconHTML

    Electroencephalogram (EEG) is able to indicate states of mental activity ranging from concentrated cognitive efforts to sleepiness. Such mental activity can be reflected by EEG energy. In particular, intrusion of EEG theta wave activity into the beta activity of active wakefulness has been interpreted as ensuing sleepiness. Pupil behavior can also provide information regarding alertness. This pape... View full abstract»

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  • Global Synchronization Criteria of Linearly Coupled Neural Network Systems With Time-Varying Coupling

    Publication Year: 2008, Page(s):319 - 332
    Cited by:  Papers (73)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (463 KB) | HTML iconHTML

    In this paper, global synchronization of linearly coupled neural network (NN) systems with time-varying coupling is investigated. The dynamical behavior of the uncoupled system at each node is general, which can be chaotic or others; the coupling configuration is time varying, i.e., the coupling matrix is not a constant matrix. Based on Lyapunov function method and the specific property of Househo... View full abstract»

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  • Recurrent Correlation Associative Memories: A Feature Space Perspective

    Publication Year: 2008, Page(s):333 - 345
    Cited by:  Papers (9)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (478 KB) | HTML iconHTML

    In this paper, we analyze a model of recurrent kernel associative memory (RKAM) recently proposed by Garcia and Moreno. We show that this model consists in a kernelization of the recurrent correlation associative memory (RCAM) of Chiueh and Goodman. In particular, using an exponential kernel, we obtain a generalization of the well-known exponential correlation associative memory (ECAM), while usin... View full abstract»

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  • Local and Global Stability Analysis of an Unsupervised Competitive Neural Network

    Publication Year: 2008, Page(s):346 - 351
    Cited by:  Papers (14)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (670 KB) | HTML iconHTML

    Unsupervised competitive neural networks (UCNN) are an established technique in pattern recognition for feature extraction and cluster analysis. A novel model of an unsupervised competitive neural network implementing a multitime scale dynamics is proposed in this letter. The local and global asymptotic stability of the equilibrium points of this continuous-time recurrent system whose weights are ... View full abstract»

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  • A Normalized Adaptive Training of Recurrent Neural Networks With Augmented Error Gradient

    Publication Year: 2008, Page(s):351 - 356
    Cited by:  Papers (9)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (282 KB) | HTML iconHTML

    For training algorithms of recurrent neural networks (RNN), convergent speed and training error are always two contradictory performances. In this letter, we propose a normalized adaptive recurrent learning (NARL) to obtain a tradeoff between transient and steady-state response. An augmented term is added to error gradient to exactly model the derivative of cost function with respect to hidden lay... View full abstract»

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  • Cline: A New Decision-Tree Family

    Publication Year: 2008, Page(s):356 - 363
    Cited by:  Papers (9)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2014 KB) | HTML iconHTML

    A new family of algorithm called Cline that provides a number of methods to construct and use multivariate decision trees is presented. We report experimental results for two types of data: synthetic data to visualize the behavior of the algorithms and publicly available eight data sets. The new methods have been tested against 23 other decision-tree construction algorithms based on benchmark data... View full abstract»

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  • Evolved Feature Weighting for Random Subspace Classifier

    Publication Year: 2008, Page(s):363 - 366
    Cited by:  Papers (11)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (243 KB) | HTML iconHTML

    The problem addressed in this letter concerns the multiclassifier generation by a random subspace method (RSM). In the RSM, the classifiers are constructed in random subspaces of the data feature space. In this letter, we propose an evolved feature weighting approach: in each subspace, the features are multiplied by a weight factor for minimizing the error rate in the training set. An efficient me... View full abstract»

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  • Stability Analysis of Markovian Jumping Stochastic Cohen–Grossberg Neural Networks With Mixed Time Delays

    Publication Year: 2008, Page(s):366 - 370
    Cited by:  Papers (160)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (227 KB) | HTML iconHTML

    In this letter, the global asymptotical stability analysis problem is considered for a class of Markovian jumping stochastic Cohen-Grossberg neural networks (CGNNs) with mixed delays including discrete delays and distributed delays. An alternative delay-dependent stability analysis result is established based on the linear matrix inequality (LMI) technique, which can easily be checked by utilizing... View full abstract»

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  • Training Two-Layered Feedforward Networks With Variable Projection Method

    Publication Year: 2008, Page(s):371 - 375
    Cited by:  Papers (18)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (614 KB) | HTML iconHTML

    The variable projection (VP) method for separable nonlinear least squares (SNLLS) is presented and incorporated into the Levenberg-Marquardt optimization algorithm for training two-layered feedforward neural networks. It is shown that the Jacobian of variable projected networks can be computed by simple modification of the backpropagation algorithm. The suggested algorithm is efficient compared to... View full abstract»

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  • Pattern Recognition (Theodoridis, S. and Koutroumbas, K.; 2006) [Book reviews]

    Publication Year: 2008, Page(s): 376
    Cited by:  Papers (1)
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  • Nearest-Neighbor Methods in Learning and Vision (Shakhnarovich, G. et al., Eds.; 2006) [Book review]

    Publication Year: 2008, Page(s): 377
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  • International Workshop on Computational Intelligence in Security for Information Systems

    Publication Year: 2008, Page(s): 378
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  • 2008 IEEE World Congress on Computational Intelligence

    Publication Year: 2008, Page(s): 379
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  • Order form for reprints

    Publication Year: 2008, Page(s): 380
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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