IEEE Transactions on Neural Networks and Learning Systems

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Publication Year: 2012, Page(s): C1
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• IEEE Transactions on Neural Networks and Learning Systems publication information

Publication Year: 2012, Page(s): C2
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• New Semi-Supervised Classification Method Based on Modified Cluster Assumption

Publication Year: 2012, Page(s):689 - 702
Cited by:  Papers (39)
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The cluster assumption, which assumes that “similar instances should share the same label,” is a basic assumption in semi-supervised classification learning, and has been found very useful in many successful semi-supervised classification methods. It is rarely noticed that when the cluster assumption is adopted, there is an implicit assumption that every instance should have a crisp ... View full abstract»

• Variational Regularized 2-D Nonnegative Matrix Factorization

Publication Year: 2012, Page(s):703 - 716
Cited by:  Papers (36)
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A novel approach for adaptive regularization of 2-D nonnegative matrix factorization is presented. The proposed matrix factorization is developed under the framework of maximum a posteriori probability and is adaptively fine-tuned using the variational approach. The method enables: (1) a generalized criterion for variable sparseness to be imposed onto the solution; and (2) prior information to be ... View full abstract»

• Neural CMOS-Integrated Circuit and Its Application to Data Classification

Publication Year: 2012, Page(s):717 - 724
Cited by:  Papers (6)
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Implementation and new applications of a tunable complementary metal-oxide-semiconductor-integrated circuit (CMOS-IC) of a recently proposed classifier core-cell (CC) are presented and tested with two different datasets. With two algorithms-one based on Fisher's linear discriminant analysis and the other based on perceptron learning, used to obtain CCs' tunable parameters-the Haberman and Iris dat... View full abstract»

• $H_{infty}$ State Estimation for Discrete-Time Complex Networks With Randomly Occurring Sensor Saturations and Randomly Varying Sensor Delays

Publication Year: 2012, Page(s):725 - 736
Cited by:  Papers (119)
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In this paper, the state estimation problem is investigated for a class of discrete time-delay nonlinear complex networks with randomly occurring phenomena from sensor measurements. The randomly occurring phenomena include randomly occurring sensor saturations (ROSSs) and randomly varying sensor delays (RVSDs) that result typically from networked environments. A novel sensor model is proposed to d... View full abstract»

• Error Analysis for Matrix Elastic-Net Regularization Algorithms

Publication Year: 2012, Page(s):737 - 748
Cited by:  Papers (15)
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Elastic-net regularization is a successful approach in statistical modeling. It can avoid large variations which occur in estimating complex models. In this paper, elastic-net regularization is extended to a more general setting, the matrix recovery (matrix completion) setting. Based on a combination of the nuclear-norm minimization and the Frobenius-norm minimization, we consider the matrix elast... View full abstract»

• Hybrid Dimensionality Reduction Method Based on Support Vector Machine and Independent Component Analysis

Publication Year: 2012, Page(s):749 - 761
Cited by:  Papers (12)
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This paper presents a new hybrid dimensionality reduction method to seek projection through optimization of both structural risk (supervised criterion) and data independence (unsupervised criterion). Classification accuracy is used as a metric to evaluate the performance of the method. By minimizing the structural risk, projection originated from the decision boundaries directly improves the class... View full abstract»

• Variational Learning for Finite Dirichlet Mixture Models and Applications

Publication Year: 2012, Page(s):762 - 774
Cited by:  Papers (35)
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In this paper, we focus on the variational learning of finite Dirichlet mixture models. Compared to other algorithms that are commonly used for mixture models (such as expectation-maximization), our approach has several advantages: first, the problem of over-fitting is prevented; furthermore, the complexity of the mixture model (i.e., the number of components) can be determined automatically and s... View full abstract»

• Class of Widely Linear Complex Kalman Filters

Publication Year: 2012, Page(s):775 - 786
Cited by:  Papers (34)
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Recently, a class of widely linear (augmented) complex-valued Kalman filters (KFs), that make use of augmented complex statistics, have been proposed for sequential state space estimation of the generality of complex signals. This was achieved in the context of neural network training, and has allowed for a unified treatment of both second-order circular and noncircular signals, that is, both thos... View full abstract»

• Chaotic Time Series Prediction Based on a Novel Robust Echo State Network

Publication Year: 2012, Page(s):787 - 799
Cited by:  Papers (86)
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In this paper, a robust recurrent neural network is presented in a Bayesian framework based on echo state mechanisms. Since the new model is capable of handling outliers in the training data set, it is termed as a robust echo state network (RESN). The RESN inherits the basic idea of ESN learning in a Bayesian framework, but replaces the commonly used Gaussian distribution with a Laplace one, which... View full abstract»

• Regularization Path for $nu$ -Support Vector Classification

Publication Year: 2012, Page(s):800 - 811
Cited by:  Papers (6)
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The v-support vector classification (v-SVC) proposed by Schölkopf has the advantage of using a regularization parameter v for controlling the number of support vectors and margin errors. However, compared to C-SVC, its formulation is more complicated, and to date there are no effective methods for computing its regularization path. In this paper, we propose a new regularization path... View full abstract»

• Discrete-Time Neural Network for Fast Solving Large Linear $L_{1}$ Estimation Problems and its Application to Image Restoration

Publication Year: 2012, Page(s):812 - 820
Cited by:  Papers (23)
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There is growing interest in solving linear L1 estimation problems for sparsity of the solution and robustness against non-Gaussian noise. This paper proposes a discrete-time neural network which can calculate large linear L1 estimation problems fast. The proposed neural network has a fixed computational step length and is proved to be globally convergent to an ... View full abstract»

• Solving the Assignment Problem Using Continuous-Time and Discrete-Time Improved Dual Networks

Publication Year: 2012, Page(s):821 - 827
Cited by:  Papers (8)
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The assignment problem is an archetypal combinatorial optimization problem. In this brief, we present a continuous-time version and a discrete-time version of the improved dual neural network (IDNN) for solving the assignment problem. Compared with most assignment networks in the literature, the two versions of IDNNs are advantageous in circuit implementation due to their simple structures. Both o... View full abstract»

• Estimator Design for Discrete-Time Switched Neural Networks With Asynchronous Switching and Time-Varying Delay

Publication Year: 2012, Page(s):827 - 834
Cited by:  Papers (78)
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This brief deals with the estimator design problem for discrete-time switched neural networks with time-varying delay. One main problem is the asynchronous-mode switching between the neuron state and the estimator. Our goal is to design a mode-dependent estimator for the switched neural networks under average dwell time switching such that the estimation error system is exponentially stable with a... View full abstract»

• Weighted Least-Squares Approach for Identification of a Reduced-Order Adaptive Neuronal Model

Publication Year: 2012, Page(s):834 - 840
Cited by:  Papers (10)
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This brief is focused on the parameter estimation problem of a second-order adaptive quadratic neuronal model. First, it is shown that the model discontinuities at the spiking instants can be recast as an impulse train driving the system dynamics. Through manipulation of the system dynamics, the membrane voltage can be obtained as a realizable model that is linear in the unknown parameters. This l... View full abstract»

• Complete Synchronization of Boolean Networks

Publication Year: 2012, Page(s):840 - 846
Cited by:  Papers (63)
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We examine complete synchronization of two deterministic Boolean networks (BNs) coupled unidirectionally in the drive-response configuration. A necessary and sufficient criterion is presented in terms of algebraic representations of BNs. As a consequence, we show that complete synchronization can occur only between two conditionally identical BNs when the transition matrix of the drive network is ... View full abstract»

• Data-Driven Cluster Reinforcement and Visualization in Sparsely-Matched Self-Organizing Maps

Publication Year: 2012, Page(s):846 - 852
Cited by:  Papers (12)
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A self-organizing map (SOM) is a self-organized projection of high-dimensional data onto a typically 2-dimensional (2-D) feature map, wherein vector similarity is implicitly translated into topological closeness in the 2-D projection. However, when there are more neurons than input patterns, it can be challenging to interpret the results, due to diffuse cluster boundaries and limitations of curren... View full abstract»

• IEEE Computational Intelligence Society Information

Publication Year: 2012, Page(s): C3
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• IEEE Transactions on Neural Networks information for authors

Publication Year: 2012, 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.

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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