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	1. Visualized Feature Fusion and Style Evaluation for Musical Genre Analysis
	YAO Qingjun,LI Haifeng,SUN Jiayin,MA Lin
	Computer Science and Technology 06 June 2010
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	Abstract:Different kinds of features in time domain, spectral domain and cepstral domain are used for musical genre classification. In this paper, through the fusion of short-term timbral features and long-term rhythmic feature, we propose a novel method where: musical genre vector is constructed using the likelihood ratio of GMM (Gaussian Mixture Model) and radar chart is applied to provide visualized style evaluation for musical genre analysis, a promising performance is achieved over our database consisting of seven different types of music. Because of the fuzzy definition of musical genres, we also investigate the music with dual-genre based on musical genre vector and radar chart.
	TO cite this article:YAO Qingjun,LI Haifeng,SUN Jiayin, et al. Visualized Feature Fusion and Style Evaluation for Musical Genre Analysis[OL].[ 6 June 2010] http://en.paper.edu.cn/en_releasepaper/content/4375363


	2. From Feature Space to Primal Space: KPCA and Its Mixture Model
	Wang Haixian
	Computer Science and Technology 20 November 2009
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	Abstract:samples, we extend KPCA to a mixture of local KPCA models by applying the mixture model to probabilistic PCA in the primal space. The theoretical analysis and experimental results on both artificial and real data set have shown the superiority of the proposed methods in terms of computational efficiency and storage space, as well as recognition rate, especially when the number of data points $n$ is large.
	TO cite this article:Wang Haixian. From Feature Space to Primal Space: KPCA and Its Mixture Model[OL].[20 November 2009] http://en.paper.edu.cn/en_releasepaper/content/36884


	3. A Cancer Recognition Method Based on DNA Microarray
	Su Qian,An Dong,Zhai Yafeng,Wang Ku,Wang Shoujue
	Computer Science and Technology 26 February 2009
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	Abstract:The accurate cancer classification is of great importance in clinical treatment. Recently, the DNA microarray technology provides a promising approach to the diagnosis and prognosis of cancer types. However, it has no perfect method for the multiclass classification problem. The difficulty lies in the fact that the data are of high dimensionality with small sample size. This paper proposed an automatic classification method of multiclass cancers based on biomimetic pattern recognition (BPR). To the public GCM data set, the average correct classification rate reaches 80% under the condition that the correct rejection rate is 81%.
	TO cite this article:Su Qian,An Dong,Zhai Yafeng, et al. A Cancer Recognition Method Based on DNA Microarray[OL].[26 February 2009] http://en.paper.edu.cn/en_releasepaper/content/29666


	4. Out-of-sample algorithm of Laplacian Eigenmaps Applied to Dimensionality Reduction
	Peng Jia,Junsong Yin,Xinsheng Huang,Dewen Hu
	Computer Science and Technology 09 April 2008
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	Abstract:The traditional nonlinear manifold learning methods have achieved great success in dimensionality reduction. However, when new samples are observed, the batch methods fail to learn them incrementally. This paper presents out-of-sample extension for Laplacian Eigenmaps, which computes the low-dimensional representation of data set by optimally preserving local neighborhood information in a certain sense. Two different incremental algorithms, the differential method and sub-manifold analysis method, are proposed. The algorithms are easy to be implemented and the computation procedure is simple. Simulation results testify the efficiency and accuracy of the proposed algorithm.
	TO cite this article:Peng Jia,Junsong Yin,Xinsheng Huang, et al. Out-of-sample algorithm of Laplacian Eigenmaps Applied to Dimensionality Reduction[OL].[ 9 April 2008] http://en.paper.edu.cn/en_releasepaper/content/20257


	5. Research on E-learner Personality Grouping Based on Fuzzy Clustering Analysis
	Tian Feng ,Wang Shibin ,Cheng Zheng,Zheng Qinghua
	Computer Science and Technology 04 January 2008
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	Abstract:Many clustering methods have been adopted by personalized e-learning system to find interested groups or common characteristics of members within the same group. However, hard boundary during discretization of collected data or subjective influences was introduced, and corresponding methods were utilized. Aiming at this problem, a fuzzy clustering method based on fuzzy statistic is proposed to cluster the learners according to their personality and learning strategy data collected from an online system. Then, an analysis method based on frequent pattern is introduced to testify the result of the proposed unsupervised clustering methods. The clustering results correspond with viewpoints of pedagogy.
	TO cite this article:Tian Feng ,Wang Shibin ,Cheng Zheng, et al. Research on E-learner Personality Grouping Based on Fuzzy Clustering Analysis[OL].[ 4 January 2008] http://en.paper.edu.cn/en_releasepaper/content/17727


	6. Research on the Segmentation of MRI Image Based on Multi-Classification Support Vector Machine
	Guo Lei ,Wu Youxi ,Liu Xuena ,Yan Weili
	Computer Science and Technology 23 May 2007
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	Abstract:In head MRI image, the boundary of each encephalic tissue is highly complicated and irregular. It is a real challenge to traditional segmentation algorithms. As a new kind of machine learning, Support Vector Machine (SVM) based on Statistical Learning Theory (SLT) has high generalization ability, especially for dataset with small number of samples in high dimensional space. SVM was originally developed for two-class classification. It is extended to solve multi-class classification problem. In this paper, 57 dimensional feature vectors for MRI image are selected as input for SVM. The segmentation of MRI image based on the Multi-Classification SVM (MCSVM) is investigated. As our experiment demonstrates, the boundaries of 7 kinds of encephalic tissues are extracted successfully, and it can reach satisfactory generalization accuracy. Thus, SVM exhibits its great potential in image segmentation.
	TO cite this article:Guo Lei ,Wu Youxi ,Liu Xuena , et al. Research on the Segmentation of MRI Image Based on Multi-Classification Support Vector Machine[OL].[23 May 2007] http://en.paper.edu.cn/en_releasepaper/content/13001

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