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Neural Information Processing, 2002. ICONIP '02. Proceedings of the 9th International Conference on

Date 18-22 Nov. 2002

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  • Improvement on regulating definition of antibody density of immune algorithm

    Publication Year: 2002, Page(s):2669 - 2672 vol.5
    Cited by:  Papers (4)
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (387 KB) | HTML iconHTML

    A new heuristic optimization algorithm, which has the advantage of keeping the diversity of the solution, immune algorithm (IA), has been developed quickly in recent years. However, the calculation of antibody density by entropy in original IA has some shortcomings, i.e. the complex calculation and constants determined by experience will slower the speed of convergence. For an advancement of a per... View full abstract»

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  • A new kernel clustering algorithm

    Publication Year: 2002, Page(s):2527 - 2531 vol.5
    Cited by:  Papers (2)
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (492 KB) | HTML iconHTML

    We propose a new kernel clustering algorithm. It estimates an in advance fixed number of vectors and margins in a feature space. Each pair of vector and margin defines a hyperplane in feature space and thus separates the data in two clusters. All the clusters together carry important information about the data set. The estimation in feature space is done implicitly by the use of a kernel. Therefor... View full abstract»

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  • Neural network algorithm for solving ray-tracing problem

    Publication Year: 2002, Page(s):2665 - 2668 vol.5
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (419 KB) | HTML iconHTML

    This work is dedicated to the study of neural network method for solving of ray-tracing task, which appears in 3D visualization algorithms. Physical representation of the task is the problem of finding the nearest point of the "vision" ray crossing with the surfaces of the scene. Application: Real time 3D visualization, rendering of the complex scenes, containing semitransparent, reflecting, diffu... View full abstract»

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  • A general formulation for support vector machines

    Publication Year: 2002, Page(s):2522 - 2526 vol.5
    Cited by:  Papers (1)
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (435 KB) | HTML iconHTML

    In this paper, we derive a general formulation of support vector machines for classification and regression respectively. Le, loss function is proposed as a patch of L1 and L2 soft margin loss functions for classifier, while soft insensitive loss function is introduced as the generalization of popular loss functions for regression. The introduction of the two loss ... View full abstract»

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  • Australian All Ordinaries Index: re-examine the utilities of the explanatory variables using three different measures

    Publication Year: 2002, Page(s):2335 - 2339 vol.5
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (512 KB) | HTML iconHTML

    The stock markets are generally nonlinear dynamic systems. Therefore, estimating stock market output depends mainly on nonlinear relationships of input variables. Additionally researchers have demonstrated that financial markets display self-similarity. To forecast such systems, a nonlinear modeling tool is required. The paper compares accuracy of prediction using the following techniques: neural ... View full abstract»

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  • Research on correct convergence of the EM algorithm for Gaussian mixtures

    Publication Year: 2002, Page(s):2660 - 2664 vol.5
    Cited by:  Papers (2)
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (484 KB) | HTML iconHTML

    In this paper, we present a theoretical analysis on the correct convergence of expectation-maximization algorithm for Gaussian mixtures. We first introduce the expectation-maximization algorithm and its general convergence properties. We also give a variation of the expectation-maximization algorithm for Gaussian mixtures. We then prove that the expectation-maximization algorithm becomes a compact... View full abstract»

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  • A low-complexity contextual Hebbian detector for blind multiuser detection

    Publication Year: 2002, Page(s):2185 - 2189 vol.5
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (616 KB) | HTML iconHTML

    This paper proposes a blind multiuser detector for CDMA systems based on a contextual Hebbian paradigm. Conventional blind detectors employ second-order statistics in their formulation, leading to first-order filter update procedure. These approaches restrict the convergence rate and tracking capability of the detectors. Hebbian learning has shown potential in handling blind source separation prob... View full abstract»

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  • On data based learning using support vector clustering

    Publication Year: 2002, Page(s):2516 - 2521 vol.5
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (636 KB) | HTML iconHTML

    This paper addresses the effect of applying clustering algorithms, based on a distance metric rule, prior to support kernel learning in classification and regression problems. Self-Organising Maps (SOMs), which place emphasis in data domain description, and K-means clustering algorithms have been selected before applying a support vector algorithm which is based on a margin rule. Moreover, the rec... View full abstract»

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  • Segmentation and recognition of on-line Pitman shorthand outlines using neural networks

    Publication Year: 2002, Page(s):2454 - 2458 vol.5
    Cited by:  Papers (2)
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (513 KB) | HTML iconHTML

    This paper presents a novel approach for the segmentation and recognition of the on-line vocalized outlines of Pitman shorthand. Due to its low redundancy, the recognition of the Pitman Shorthand requires high-performance outline segmentation and stroke classification. Our approach includes (1) the segmentation of the vocalized outlines, including the detection of over-segmentation using a neural ... View full abstract»

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  • Rule extraction from technology IPOs in the US stock market

    Publication Year: 2002, Page(s):2328 - 2334 vol.5
    Cited by:  Papers (1)
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (720 KB) | HTML iconHTML

    Machine learning techniques for prediction and rule extraction from artificial neural network methods are used. The hypothesis that market sentiment and IPO specific attributes are equally responsible for first-day IPO returns in the US stock market is tested. Machine learning methods used are Bayesian classifications, support vector machines, decision tree techniques, rule learners and artificial... View full abstract»

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  • Modelling iterative roots of mappings in multidimensional spaces

    Publication Year: 2002, Page(s):2655 - 2659 vol.5
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (505 KB) | HTML iconHTML

    Solutions φ(x) of the functional equation φ(φ(x)) = f (x) are called iterative roots of the given function f(x). They are of interest in dynamical systems, chaos and complexity theory and also in the modelling of certain industrial and financial processes. The problem of computing this "square root" in function (or operator) spaces remains a hard task and is, for the general case, stil... View full abstract»

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  • Spectrum estimation by using of neural network method

    Publication Year: 2002, Page(s):2181 - 2184 vol.5
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (507 KB) | HTML iconHTML

    Spectral estimation has been applied broadly in the signal-processing domain. In modern spectral estimation methods, the model parameters will be obtained by solving the Yule-Walker equations. There are several common disadvantages such as complicated processing steps and heavy calculation load in all the optimized algorithms. In this paper, two methods using neural networks are discussed. In the ... View full abstract»

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  • A stochastic nonlinear evolution model of neuronal activity with random amplitude

    Publication Year: 2002, Page(s):2497 - 2501 vol.5
    Cited by:  Papers (3)
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (470 KB) | HTML iconHTML

    In this paper we propose a new stochastic nonlinear evolution model with the stochastic amplitude in neuronal activities to obtain the average number density, which is used to describe the neurocommunication among population of neurons. The average number density is a function of the amplitude, phase and time. The number density of the diffusion process of neurocommunication is given for the activ... View full abstract»

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  • Accelerated reinforcement learning control using modified CMAC neural networks

    Publication Year: 2002, Page(s):2575 - 2578 vol.5
    Cited by:  Papers (3)
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (417 KB) | HTML iconHTML

    Reinforcement learning is a class of model-free learning control methods that can solve Markov decision problems. One difficulty for the application of reinforcement learning control is its slow convergence, especially in MDPs with continuous state space. In this paper, a modified structure of CMAC neural networks is proposed to accelerate reinforcement learning control. The modified structure is ... View full abstract»

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  • Neural networks and the classification of mineralogical samples using x-ray spectra

    Publication Year: 2002, Page(s):2683 - 2687 vol.5
    Cited by:  Papers (1)
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (593 KB) | HTML iconHTML

    The automatic classification of large numbers of mineral samples is a practical problem in mining research. A system currently in use is based on simple statistical tests. Although the system performs well under typical conditions, the data collection procedure can be very time-consuming. This time can be significantly reduced, but at a cost of introducing noise into the data, leading to a degrada... View full abstract»

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  • Kernel methods for identification faces

    Publication Year: 2002, Page(s):2512 - 2515 vol.5
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (442 KB) | HTML iconHTML

    We review a neural network implementation of the statistical technique of Principal Component Analysis (PCA) and Factor Analysis. We now derive a new method based on Kernel Principal Components Analysis (KPCA) and extend the Kernel PCA method to sparsified Kernel PCA. We then apply two methods to the data set which is composed of 10 faces in a mixture of poses. We wish to identify only the most si... View full abstract»

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  • Intra-feature metric matrices for nominal data pattern classification

    Publication Year: 2002, Page(s):2587 - 2591 vol.5
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (434 KB) | HTML iconHTML

    In machine learning problems, similarity measures (e.g. using metrics) are widely utilized in nearest neighbor, support vector machines and neural network algorithms. However, when there is one or more non-ordinal data in feature vector, metric evaluation is difficult. Although non-metric methods such as decision trees with ID3, C4.5 or CART can be employed to process such problems, many other ele... View full abstract»

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  • Decision of image watermarking strength based on artificial neural-networks

    Publication Year: 2002, Page(s):2430 - 2434 vol.5
    Cited by:  Papers (5)
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (937 KB) | HTML iconHTML

    Digital watermarking is a new technique for digital multimedia copyright protection. The robustness and the imperceptibility are the basic requirements of the digital watermark. The key factor that affects both the robustness and the imperceptibility of the digital watermark is the watermarking strength. In this paper, artificial neural network (ANN) is used to model human visual system (HVS) and ... View full abstract»

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  • Estimation of inner information representations in time series prediction and bi-directionalization effect of computing architecture

    Publication Year: 2002, Page(s):2147 - 2151 vol.5
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (541 KB) | HTML iconHTML

    A bi-directional computing architecture for time series prediction, which computes not only the future prediction transformation but also the past prediction one, is proposed recently and applied to several prediction tasks. According to the previous studies, an improvement of the prediction performances has been observed with different kinds of data sets. Nevertheless, its detailed mechanism for ... View full abstract»

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  • Dynamical scaling in on-line hand-written characters' matching

    Publication Year: 2002, Page(s):2449 - 2453 vol.5
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (447 KB) | HTML iconHTML

    On-line signature verification and/or recognition of handwritten characters are becoming more and more important in the network society. When these problems are considered, one fundamental method will be to find the corresponding points between the test one and the template. For this purpose, we proposed a matching method using DP (dynamic programming) matching and dynamical scaling parameters. Th... View full abstract»

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  • Multilayered and columnar competitive networks for spoken word recognition

    Publication Year: 2002, Page(s):2223 - 2227 vol.5
    Cited by:  Patents (4)
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (444 KB) | HTML iconHTML

    We have presented a multilayered and columnar competitive network involving competitive associative nets (CANs) and adaptive vector quantization nets (AVQNs) for spoken word recognition. Although the network has shown good performance in recognition rate, it requires a relatively large calculation time owing to the CANs. So, here, we present a new network replacing the CANs by a conventional featu... View full abstract»

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  • An intelligent system for personal and family financial service

    Publication Year: 2002, Page(s):2325 - 2327 vol.5
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (289 KB) | HTML iconHTML

    An intelligent system for personal and family financial service is designed. The technologies of data mining, data warehouses, neural networks and expert systems are applied in different moduli of the system. This system is designed for bank representatives to serve their clients by providing the case-based financial plan according to client's request. After collecting necessary information from t... View full abstract»

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  • A services-oriented architecture applied to artificial neural network

    Publication Year: 2002, Page(s):2650 - 2654 vol.5
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (546 KB) | HTML iconHTML

    In this work, a services-oriented architecture that allows the utilization of artificial neural network models, through the Internet, is proposed. The Web Services, the Message Queuing and the Neural Network Markup Language constitute the technology used for the development of this approach. The module called by Artificial Neural Network-Web Service is the main module of the proposed architecture.... View full abstract»

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  • Sequential chaotic annealing neural network for CDMA multiuser detection

    Publication Year: 2002, Page(s):2176 - 2180 vol.5
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (453 KB) | HTML iconHTML

    The optimum multiuser detection problem in the presence of noise is a quadratic integer programming problem which has been shown to be NP-complete. In this paper, we propose a novel neural network based multiuser detector for a direct-sequence code division multiple access system. The proposed detector combines the paradigms of chaotic neural networks and methods from sequential unconstrained mini... View full abstract»

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  • A comparison study on protein fold recognition

    Publication Year: 2002, Page(s):2492 - 2496 vol.5
    Cited by:  Papers (9)
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (509 KB) | HTML iconHTML

    Although two proteins may be structurally similar, they may not have significant sequence similarity. The recognition of protein fold structures without relying on sequence similarity is a complex task. This work presents a comparison study on the recognition of 3-dimensional protein folds by Machine Learning models. Combinations of neural networks were trained by bagging and arcing with two datas... View full abstract»

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