By Topic

# IEEE Transactions on Neural Networks

## Filter Results

Displaying Results 1 - 20 of 20

Publication Year: 2010, Page(s): C1
| PDF (114 KB)
• ### IEEE Transactions on Neural Networks publication information

Publication Year: 2010, Page(s): C2
| PDF (39 KB)
• ### Neuro-Adaptive Force/Position Control With Prescribed Performance and Guaranteed Contact Maintenance

Publication Year: 2010, Page(s):1857 - 1868
Cited by:  Papers (34)
| | PDF (759 KB) | HTML

In this paper, we address unresolved issues in robot force/position tracking including the concurrent satisfaction of contact maintenance, lack of overshoot, desired speed of response, as well as accuracy level. The control objective is satisfied under uncertainties in the force deformation model and disturbances acting at the joints. The unknown nonlinearities that arise owing to the uncertaintie... View full abstract»

• ### Stability Analysis of Multiplicative Update Algorithms and Application to Nonnegative Matrix Factorization

Publication Year: 2010, Page(s):1869 - 1881
Cited by:  Papers (23)
| | PDF (436 KB) | HTML

Multiplicative update algorithms have proved to be a great success in solving optimization problems with nonnegativity constraints, such as the famous nonnegative matrix factorization (NMF) and its many variants. However, despite several years of research on the topic, the understanding of their convergence properties is still to be improved. In this paper, we show that Lyapunov's stability theory... View full abstract»

• ### Computing and Analyzing the Sensitivity of MLP Due to the Errors of the i.i.d. Inputs and Weights Based on CLT

Publication Year: 2010, Page(s):1882 - 1891
Cited by:  Papers (8)
| | PDF (875 KB) | HTML

In this paper, we propose an algorithm based on the central limit theorem to compute the sensitivity of the multilayer perceptron (MLP) due to the errors of the inputs and weights. For simplicity and practicality, all inputs and weights studied here are independently identically distributed (i.i.d.). The theoretical results derived from the proposed algorithm show that the sensitivity of the MLP i... View full abstract»

• ### Decoding Stimulus-Reward Pairing From Local Field Potentials Recorded From Monkey Visual Cortex

Publication Year: 2010, Page(s):1892 - 1902
Cited by:  Papers (8)
| | PDF (1633 KB) | HTML

Single-trial decoding of brain recordings is a real challenge, since it pushes the signal-to-noise ratio issue to the limit. In this paper, we concentrate on the single-trial decoding of stimulus-reward pairing from local field potentials (LFPs) recorded chronically in the visual cortical area V4 of monkeys during a perceptual conditioning task. We developed a set of physiologically meaningful fea... View full abstract»

• ### Condensed Vector Machines: Learning Fast Machine for Large Data

Publication Year: 2010, Page(s):1903 - 1914
Cited by:  Papers (5)
| | PDF (647 KB) | HTML

Scalability is one of the main challenges for kernel-based methods and support vector machines (SVMs). The quadratic demand in memory for storing kernel matrices makes it impossible for training on million-size data. Sophisticated decomposition algorithms have been proposed to efficiently train SVMs using only important examples, which ideally are the final support vectors (SVs). However, the abil... View full abstract»

• ### On the Improvement of Neural Cryptography Using Erroneous Transmitted Information With Error Prediction

Publication Year: 2010, Page(s):1915 - 1924
Cited by:  Papers (4)
| | PDF (618 KB) | HTML

Neural cryptography deals with the problem of “key exchange” between two neural networks using the mutual learning concept. The two networks exchange their outputs (in bits) and the key between the two communicating parties is eventually represented in the final learned weights, when the two networks are said to be synchronized. Security of neural synchronization is put at risk if an... View full abstract»

• ### Multiple View Clustering Using a Weighted Combination of Exemplar-Based Mixture Models

Publication Year: 2010, Page(s):1925 - 1938
Cited by:  Papers (18)
| | PDF (437 KB) | HTML

Multiview clustering partitions a dataset into groups by simultaneously considering multiple representations (views) for the same instances. Hence, the information available in all views is exploited and this may substantially improve the clustering result obtained by using a single representation. Usually, in multiview algorithms all views are considered equally important, something that may lead... View full abstract»

• ### Periodic Activation Function and a Modified Learning Algorithm for the Multivalued Neuron

Publication Year: 2010, Page(s):1939 - 1949
Cited by:  Papers (17)
| | PDF (473 KB) | HTML

In this paper, we consider a new periodic activation function for the multivalued neuron (MVN). The MVN is a neuron with complex-valued weights and inputs/output, which are located on the unit circle. Although the MVN outperforms many other neurons and MVN-based neural networks have shown their high potential, the MVN still has a limited capability of learning highly nonlinear functions. A periodi... View full abstract»

• ### Optimization Methods for Spiking Neurons and Networks

Publication Year: 2010, Page(s):1950 - 1962
Cited by:  Papers (16)
| | PDF (1005 KB) | HTML

Spiking neurons and spiking neural circuits are finding uses in a multitude of tasks such as robotic locomotion control, neuroprosthetics, visual sensory processing, and audition. The desired neural output is achieved through the use of complex neuron models, or by combining multiple simple neurons into a network. In either case, a means for configuring the neuron or neural circuit is required. Ma... View full abstract»

• ### Mixing Linear SVMs for Nonlinear Classification

Publication Year: 2010, Page(s):1963 - 1975
Cited by:  Papers (26)
| | PDF (714 KB) | HTML

In this paper, we address the problem of combining linear support vector machines (SVMs) for classification of large-scale nonlinear datasets. The motivation is to exploit both the efficiency of linear SVMs (LSVMs) in learning and prediction and the power of nonlinear SVMs in classification. To this end, we develop a LSVM mixture model that exploits a divide-and-conquer strategy by partitioning th... View full abstract»

• ### Robust Curve Clustering Based on a Multivariate $t$-Distribution Model

Publication Year: 2010, Page(s):1976 - 1984
Cited by:  Papers (1)
| | PDF (723 KB) | HTML

This brief presents a curve clustering technique based on a new multivariate model. Instead of the usual Gaussian random effect model, our method uses the multivariate -distribution model which has better robustness to outliers and noise. In our method, we use the B-spline curve to model curve data and apply the mixed-effects model to capture the randomness and covariance of all curves within the ... View full abstract»

Publication Year: 2010, Page(s):1984 - 1990
Cited by:  Papers (23)
| | PDF (325 KB) | HTML

Nearest prototype methods are a successful trend of many pattern classification tasks. However, they present several shortcomings such as time response, noise sensitivity, and storage requirements. Data reduction techniques are suitable to alleviate these drawbacks. Prototype generation is an appropriate process for data reduction, which allows the fitting of a dataset for nearest neighbor (NN) cl... View full abstract»

• ### Linear Discriminant Analysis for Signatures

Publication Year: 2010, Page(s):1990 - 1996
Cited by:  Papers (5)
| | PDF (183 KB) | HTML

We propose signature linear discriminant analysis (signature-LDA) as an extension of LDA that can be applied to signatures, which are known to be more informative representations of local image features than vector representations, such as visual word histograms. Based on earth mover's distances between signatures, signature-LDA does not require vectorization of local image features in contrast to... View full abstract»

• ### 2010 Index IEEE Transactions on Neural Networks Vol. 21

Publication Year: 2010, Page(s):1997 - 2018
| PDF (221 KB)
• ### Call for papers IEEE Transactions on Neural Networks Special Issue: Online Learning in Kernel Methods

Publication Year: 2010, Page(s): 2019
| PDF (150 KB)
• ### Access over 1 million articles - The IEEE Digital Library [advertisement]

Publication Year: 2010, Page(s): 2020
| PDF (370 KB)
• ### IEEE Computational Intelligence Society Information

Publication Year: 2010, Page(s): C3
| PDF (37 KB)
• ### IEEE Transactions on Neural Networks Information for authors

Publication Year: 2010, Page(s): C4
| PDF (39 KB)

## 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