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2008 Fourth International Conference on Natural Computation

Date 18-20 Oct. 2008

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  • [Front cover - Vol 7]

    Publication Year: 2008, Page(s): C1
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  • [Title Page i - Volume 7]

    Publication Year: 2008, Page(s): i
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  • [Title page iii - Volume 7]

    Publication Year: 2008, Page(s): iii
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  • [Copyright notice - Volume 7]

    Publication Year: 2008, Page(s): iv
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  • Table of contents - Volume 7

    Publication Year: 2008, Page(s):v - xiv
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  • Preface - Volume 7

    Publication Year: 2008, Page(s): xv
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  • Conference organizers

    Publication Year: 2008, Page(s): xvi
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  • Programme Committee - Volume 7

    Publication Year: 2008, Page(s):xvii - xviii
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  • A Business Intelligent Model for Market Risk Measurement

    Publication Year: 2008, Page(s):3 - 7
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (352 KB) | HTML iconHTML

    In this study, we propose a business intelligent model integrating econometric models, i.e. ARMA, GARCH, and ANN models for VaR estimation. The business intelligent model achieves better efficiency in input variables selecting because they are selected and newly created by time series models. Repetitive trial error process could be effectively eliminated to one time series process. On the other ha... View full abstract»

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  • A Hybrid Credit Scoring Model Based on Genetic Programming and Support Vector Machines

    Publication Year: 2008, Page(s):8 - 12
    Cited by:  Papers (6)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (444 KB) | HTML iconHTML

    Credit scoring has obtained more and more attention as the credit industry can benefit from reducing potential risks. Hence, many different useful techniques, known as the credit scoring models, have been developed by the banks and researchers in order to solve the problems involved during the evaluation process. In this paper, a hybrid credit scoring model (HCSM) is developed to deal with the cre... View full abstract»

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  • A Least Squares Bilateral-Weighted Fuzzy SVM Method to Evaluate Credit Risk

    Publication Year: 2008, Page(s):13 - 17
    Cited by:  Papers (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (334 KB) | HTML iconHTML

    In this study, we propose a least squares bilateral-weighted fuzzy support vector machine (LS-BFSVM) method to evaluate the credit risk problem. The method can not only reduce the computational complexity by considering equality constraints instead of inequalities for the classification problem with a formulation in least squares sense, but also increase the training algorithm's generalization abi... View full abstract»

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  • A Method of Discovering Patterns to Predict Specified Events from Financial Time Series

    Publication Year: 2008, Page(s):18 - 22
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (336 KB) | HTML iconHTML

    This paper proposes a method to discover time series patterns predictive of the occurrence of specified events. The process of technique realization is mainly composed of four steps: specifying interest function and parameters; searching ODs, clustering ODs for candidate patterns and identifying patterns. A simulation study is conducted as verification. And an application study in the stock market... View full abstract»

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  • Forecasting Chinese Stock Markets Volatility Based on Neural Network Combining

    Publication Year: 2008, Page(s):23 - 27
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (380 KB) | HTML iconHTML

    Volatility plays a key role in asset and portfolio management and derivatives pricing. As such, more accurate measures and better forecasts of volatility are crucial for the implementation and evaluation of asset and derivative pricing models in addition to trading and hedging strategies. However, whilst GARCH models are able to capture the observed clustering effect in asset price volatility, the... View full abstract»

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  • Foreign Exchange Rates Forecasting with Multilayer Perceptrons Neural Network by Bayesian Learning

    Publication Year: 2008, Page(s):28 - 32
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (335 KB) | HTML iconHTML

    In order to avoid the over-fitting in the training of neural networks, we apply Bayesian learning to neural networks. We illustrate the advantages of Bayesian learning by concentrating on multilayer perceptrons (MLP) neural networks and Markov Chain Monte Carlo (MCMC) method for computing the integrations. We conduct the experiments on the foreign exchange rate forecasting by using the approach. T... View full abstract»

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  • Integrating Item Category Information in Collaborative Filtering Recommender Algorithm

    Publication Year: 2008, Page(s):33 - 38
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (288 KB) | HTML iconHTML

    To produce high quality recommendations and achieve high coverage in the face of data sparsity in recommender systems, we explore category-based adjusted conditional probability similarity (CACPS) collaborative filtering technique in this paper. CACPS technique firstly analyzes the user-item matrix to identify relationships between different items, and then uses these relationships to indirectly c... View full abstract»

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  • Investigation of Diversity Strategies in SVM Ensemble Learning

    Publication Year: 2008, Page(s):39 - 42
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (382 KB) | HTML iconHTML

    In SVM ensemble learning, diversity strategy is one of the most important determinants to obtain good performance. In order to examine and analyze the impacts of diversity strategies on SVM ensemble learning, this study tries to make such a deep investigation by taking credit scoring as an illustrative example. Experimental results found that the accuracy of ensemble models will be increased if en... View full abstract»

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  • Multi Scale Nonlinear Ensemble Model for Foreign Exchange Rate Prediction

    Publication Year: 2008, Page(s):43 - 47
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (299 KB) | HTML iconHTML

    This paper proposes a novel multi scale nonlinear ensemble methodology for analyzing and modeling the complex exchange rate behaviors. Using several techniques integrated under the proposed unified framework, it deals with data characteristics such as autocorrelation, multi scale heterogeneity and parameter instability during the modeling process. The multi scale heterogeneity property is modeled ... View full abstract»

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  • Spectra Analysis of Sampling and Reconstructing Continuous Signal Using Hamming Window Function

    Publication Year: 2008, Page(s):48 - 52
    Cited by:  Papers (4)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (328 KB) | HTML iconHTML

    Hamming window function was applied to studying sampling theorem. A continuous band-limited spectrum function F(w) was constructed with Hamming window function. Its corresponding time-domain signal f(t) was worked out by inverse Fourier transform. f(t) was sampled with a comb function dT(t). By modifying the value of T, all kinds of sampling signals were produced, including critical, over and unde... View full abstract»

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  • SVM Combined with FCM and PCA for Financial Diagnosis

    Publication Year: 2008, Page(s):53 - 57
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (443 KB) | HTML iconHTML

    Financial diagnosis is an important and widely studied topic in the last three decades. Recently, the support vector machine (SVM) has been applied to the problem of financial diagnosis. Fuzzy c-means clustering (FCM) is among considerable techniques for data reduction. In addition, principal component analysis (PCA) is a powerful technique for feather extraction. This paper proposes using fuzzy c... View full abstract»

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  • Active Learning for kNN Based on Bagging Features

    Publication Year: 2008, Page(s):61 - 64
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (364 KB) | HTML iconHTML

    Ensemble methods that train multiple learners and then combine their predictions have been shown to be very effective in supervised learning. But bagging does not work very well in some case, such as k-nearest neighbor (kNN). At the same time, query learning strategies using bagging is also not work very well. From features view, we introduce bagging features active learning (ALBF) for kNN and app... View full abstract»

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  • An Overview on Sensory and Multi-Sensory Analysis and Related Industrial Applications

    Publication Year: 2008, Page(s):65 - 69
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    Sensory and multi-sensory analysis has become a powerful tool for helping industrial companies in product design, quality inspection and marketing exploitation. This approach permits to formalize and characterize some complex concepts such as comfort, well-being and sustainable development. However, developing a suitable sense measuring device, an efficient working procedure and a powerful computi... View full abstract»

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  • Computer Aided Blend Design: Concept, Framework and Key Technologies

    Publication Year: 2008, Page(s):70 - 74
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (360 KB) | HTML iconHTML

    Computer aided blend design is a complex and novel research field. In this paper, we define the concept, connotation, purpose, characteristics of computer aided blend design comprehensively, put forward a technologies framework about computer aided blend design, and deeply analyze the key technologies including correlation analysis, intelligent sensory evaluation and blend optimized design etc. In... View full abstract»

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  • Determination of Degree of Relevancy of Design Elements and Fashion Image with Semantic Network

    Publication Year: 2008, Page(s):75 - 79
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (136 KB) | HTML iconHTML

    A customer has idea of different design elements associated with the garment, which he wants to buy. A questionnaire using items, which gives the representation of design elements, is prepared for fashion images. The representation of design elements is divided into several sections at different level. The relevancy between design elements and fashion image is determined by semantic network, in wh... View full abstract»

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  • Formalization of Fashion Sensory Data Based on Fuzzy Set Theory

    Publication Year: 2008, Page(s):80 - 84
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (420 KB) | HTML iconHTML

    Sensory Engineering (SE) was applied in fashion industry for market exploring, consumer behavior evaluation and personalized product designing. The consumer perceptions on products were investigated and analyzed. The fashion sensory data were established for style, color, image according to the results of investigation and analysis. The expert systems based on fuzzy set theory was developed to des... View full abstract»

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  • Fuzzy Logic of Matching Sense on Fashion Image

    Publication Year: 2008, Page(s):85 - 89
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (508 KB) | HTML iconHTML

    Fashion products contain lots of sensory information, such as style, color, matching of clothing and accessories, etc. Sensory Engineering is applied as an approach for garment industry to improve service of CRM (customer resource management), to provide personalized design frame work, to establish fashion E-retail and decision support system. It can be used for quality inspection, product design ... View full abstract»

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