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The applicability of biased estimation in model and model order selection

The applicability of biased estimation in model and model or...

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Weaam Alkhaldi D. Robert Iskander Abdelhak M. Zoubir Signal Processing Group Institute of Communications Technische Universität Darmstadt Darmstadt Germany Contact Lens & Visual Optics Laboratory School of Optometry Queensland University of Technology Brisbane Australia

Biased estimation has the advantage of reducing the mean squared error (MSE) of an estimator. The question of interest is how biased estimation affects model selection. In this paper, we introduce biased estimation to a range of model selection criteria. Specifically, we analyze the performance of the minimum description length (MDL) criterion based on biased and unbiased estimation and compare it against modern model selection criteria such as Kay's conditional model order estimator (CME), the bootstrap and the more recently proposed hook-and-loop resampling based model selection. The advantages and limitations of the considered techniques are discussed. The results indicate that, in some cases, biased estimators can slightly improve the selection of the correct model. We also give an example for which the CME with an unbiased estimator fails, but could regain its power when a biased estimator is used.

关键词： Context modeling Optical signal processing Covariance matrix Lenses Kelvin Australia Performance analysis Power engineering and energy Equations Vectors

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On the computer intensive methods in model selection

On the computer intensive methods in model selection

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： D. Robert Iskander Weaam Alkhaldi Abdelhak M. Zoubir Contact Lens & Visual Optics Laboratory School of Optometry Queensland University of Technology Brisbane Australia Signal Processing Group Institute for Communications Technische Universität Darmstadt Darmstadt Germany

Bootstrap-based model selection has been shown in many practical instances to be superior to classical methods such the AIC and MDL. This is particularly noticeable when the distribution of modelling noise is unknown and/or when the available data samples are small. One of the main problems of using bootstrap model selection with real data is the necessity of tuning the residual scaling parameter or estimating the length of a sub-sample. Recently, we have developed a new hook and loop (HL) resampling plane, in which the scaling of the residuals is avoided. Here, we compare the performance of the range of resampling planes that can be used in the context of model selection and show that the HL-based model selection is superior to its predecessors. Moreover, in the context of fitting parametric models to corneal data measured by videokeratoscopes, the HL provides results that are consistent with clinical expectations.

关键词： Context modeling Optical signal processing Optical computing Lenses Nonlinear optics Kelvin Australia Parameter estimation Parametric statistics Surface fitting

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THE "HOOK AND LOOP" RESAMPLING PLANE

THE "HOOK AND LOOP" RESAMPLING PLANE

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2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive processing (CAMPSAP 2007)

作者： D. Robert Iskander Weaam Alkhaldi Contact Lens & Visual Optics Laboratory School of Optometry Queensland University of Technology Brisbane QLD Australia Signal Processing Group Institute for Communications Technische Universitat Darmstadt Darmstadt Germany

We propose a new resampling scheme that takes literally the concept of the non-parametric bootstrap in which new samples are generated from the empirical distribution function. The introduced resampling concept is totally heuristic, but already shows promising results when applied to model selection. We show that for a range of linear models, the proposed resampling scheme outperforms the classical model selection techniques as well as its predecessor, the non-parametric bootstrap. It also simplifies the practical problem of choosing residual scaling or the length of the subsample that exists in the traditional bootstrap based model selection approach.

关键词： Yttrium Equations Lenses Optical signal processing Kelvin Australia Distribution functions signal processing Random variables

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Agent swarm classification network ASCN

Agent swarm classification network ASCN

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Chi-kin Chow Hung-tat Tsui Visual Signal Processing and Communications Laboratory Department of Electronic Engineering Chinese University of Hong Kong Hong Kong China

ISBN: (纸本)0780385675

In this paper we introduced a new RBF classification network - "agent swarm classification network ASCN", which is trained by a multi-agent system (MAS) approach. MAS can be regarded as a swarm of independent software agents interact with each other to achieve common goals, complete concurrent distributed tasks under autonomous control. By treating each neuron as an agent, the weights of neurons can be determined through a set of pre-defined simple agent behavior. Three sets of experiments are examined to observe the effectiveness of the proposed method.

关键词： Multiagent systems Neurons Radial basis function networks Speech recognition Image recognition Low earth orbit satellites signal processing Laboratories Neural networks Image processing

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A relational multi-objective genetic algorithm

A relational multi-objective genetic algorithm

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International Joint Conference on Neural Networks (IJCNN)

作者： Sum-wai Lee Hung-tat Tsui Visual Signal Processing and Communications Laboratory Department of Electronic Engineering Chinese University of Hong Kong Hong Kong China

ISBN: (纸本)0780383591

We propose a new relational multi-objective genetic algorithm (RMOGA) with a novel generic operator, inheritance. It is found that inheritance have very good performance when used with a multi-objective genetic algorithm. Its characteristic is similar to a crossover operator. However, it aims to exchange the mathematical relationships, but not values, between two selected sub-chromosomes. We also propose to use a Pareto-ranking to help form a fitness function. Then it is optimized by the relational multi-objective genetic algorithm, which includes the new operator. The advantage of this approach is its ability to inherit the best relationships. To test the effectiveness of the proposed methodology, experiments on using it for assembling broken objects were performed. Excellent results are obtained.

关键词： Genetic algorithms Assembly Biological cells signal processing algorithms Laboratories Genetic engineering Testing Performance evaluation Genetic programming Genetic mutations

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Autonomous agent response learning by a multi-species particle swarm optimization

Autonomous agent response learning by a multi-species partic...

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Congress on Evolutionary Computation

作者： Chi-kin Chow Hung-tat Tsui Visual Signal Processing and Communications Laboratory Department of Electronic Engineering Chinese University of Hong Kong Hong Kong China

ISBN: (纸本)0780385152

An autonomous agent response learning (AARL) algorithm is presented in this paper. We proposed to decompose the award function into a set of local award functions. By optimizing this objective function set, the response function with maximum award can be determined. To tackle the optimization problem, a modified particle swarm optimization (PSO) called "multi-species PSO (MS-PSO)" is introduced by considering each objective function as a specie swarm. Two sets of experiments are provided to illustrate the performance of MS-PSO. The results show that it returns a more accurate response set within shorter duration by comparing with other PSO methods.

关键词： Autonomous agents Particle swarm optimization signal processing algorithms Neural networks Constraint optimization Optimization methods Laboratories Robotics and automation Manufacturing automation Remotely operated vehicles

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Image enlargement as an edge estimation

Image enlargement as an edge estimation

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International Joint Conference on Neural Networks (IJCNN)

作者： Chi-kin Chow Hung-tat Tsui Visual Signal Processing and Communications Laboratory Department of Electronic Engineering Chinese University of Hong Kong Hong Kong China

ISBN: (纸本)0780383591

A robust image enlargement algorithm is presented in this paper. We formulate the image enlargement process as an edge information estimation process. In order to achieve a higher resolution, we first perform pixel duplication [W.K. Pratt, 1991] on the target image to form an initial high resolution image. Then the edge details of the enlarged image are estimated by using a novel neural network called "agent swarm regression network ASRN", which is trained by a set of low resolution (LR) / high resolution (HR) image patch pairs. Two benchmark images were used to verify the performance of the proposed algorithm. The results show that the enlarged images by the proposed algorithm are sharper than those by the conventional methods.

关键词： Image resolution signal processing algorithms Pixel Neural networks Robustness Interpolation Artificial neural networks Laboratories Spatial resolution Frequency

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Agent swarm regression network ASRN

Agent swarm regression network ASRN

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IEEE International Conference on Systems, Man and Cybernetics

作者： Chi-kin Chow Hung-tat Tsui Visual Signal Processing and Communications Laboratory Department of Electronic Engineering Chinese University of Hong Kong Hong Kong China

ISBN: (纸本)0780385675

A multi-agent system (MAS), with independent software agents interacting with each other to achieve common goals will complete concurrent distributed tasks under autonomous control. In this paper, novel RBF regression network ''agent swarm regression network ASRN" is proposed and trained by a MAS. Each neuron of the ASRN is considered as an agent, which consists of per-defined simple agent behavior set. After a sufficient number of iterations, the weights of neurons can be determined. Two sets of experiment are examined to observe the effectiveness of the proposed method.

关键词： Support vector machines Multiagent systems Neurons Radial basis function networks Control systems Low earth orbit satellites signal processing Laboratories Communication system control Interpolation

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Global motion estimation using modified least median of square error

Global motion estimation using modified least median of squa...

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International Symposium on Intelligent Multimedia, Video and Speech processing

作者： C.M. Mak W.K. Cham Visual Signal Processing & Communications Laboratory Department of Electronic Engineering Chinese University of Hong Kong Hong Kong China

Global motion estimation is an important step in video coding. It is needed in automatic video object segmentation for content-based video coding and useful in motion compensation. Among the many methods for global motion estimation, the least median of square error (LMedS) is one of the best because of its robustness in outlier rejection. However, its performance deteriorates if the number of error medians computed is not sufficient or outliers occupy more than 50% of the total pixels. In this paper, we show how the number of medians affects the performance and that allows one to obtain a good tradeoff between performance and computation requirement. Also, we have developed a new method that allows LMedS to maintain its excellent performance when outliers occupy more than 50% of the total pixels.

关键词： Motion estimation Layout Video signal processing Object segmentation Video coding Robustness Tiles Least squares approximation Video sequences Cameras

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Sequential RBF function estimator: memory regression network

Sequential RBF function estimator: memory regression network

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IEEE International Conference on Systems, Man and Cybernetics

作者： Chi-kin Chow Hung-tat Tsui Visual Signal Processing and Communications Laboratory Departmenl of Electronic Engineering Chinese University of Hong Kong Hong Kong China

The neural-network training algorithm can be divided into 2 categories: (1) batch mode and (2) sequential mode. In this paper, a novel online RBF network called "memory regression network (MRN)" is proposed. Different from the previous approaches (de Freitas, N, et al., Aug. 1999), (Schiffman, W, et al., 1993), MRN involves two types of memories: experience and neuron, which handle short and long term memories respectively. By simulating human's learning behavior, a given function can be estimated without memorizing the whole training set. Two sets of function estimation experiments are examined in order to illustrate the performance of the proposed algorithm. The results show that MRN can effectively approximate the given function within a reasonable time and acceptable mean square error

关键词： Neurons Neural networks signal processing algorithms Radial basis function networks Radio access networks Robots Laboratories Humans Interpolation Image processing

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