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Research of Knowledge Acquisition and System Development by Data Mining

数据挖掘知识获取与系统开发研究

基本信息

批准号：
16360199
负责人：
ABE Shigeo
金额：
$ 9.66万
依托单位：
Kobe University
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (B)
财政年份：
2004
资助国家：
日本
起止时间：
2004 至 2005
项目状态：
已结题

项目摘要

We conducted research on knowledge acquisition and system development and got the following results :1. Knowledge acquisition and system development under steady state environments(1) We have developed a clustering method using support vector machines (SVMs) by dividing the image data in segments and feature selection method using SVM ensembles.(2) We have developed an incremental training method that keeps only support vector candidates and demonstrated that the generalization ability was maintained while deleting training data.2. Knowledge acquisition by data mining and system development under dynamic environments(1) Dynamic feature space learning : we have developed incremental learning algorithms of Principal Component Analysis (PCA) and Kernel PCA (KPCA), and demonstrated that the feature selection was successfully carried out by adapting to the variation of data distributions.(2) System development : We proposed a method to integrate the developed dynamic feature selection algor … More ithm into a classifier model, in which the k-nearest neighbor method was combined with a dynamic clustering algorithm, and a neural network model. We demonstrated that the proposed method enabled the classifier to conduct stable incremental learning, and that the developed system had excellent performance for not only bench-mark datasets but also facial recognition datasets. Although we tried to develop an incremental SVM system, it has not been completed yet. This is reserved as our future work.3. Development of image segmentation system by data mining(1)Development of clustering method for image segmentation : we have developed a basic system to detect and classify the uncertain object imagery and demonstrated that the detection and classification of crystal imagery was properly carried out using image database of protein crystallizations.(2) Development of feature extraction method for image segmentation : we have developed a feature extraction method based on the multiresolution spectral histograms by wavelet transformation and demonstrated that the proposed feature is available for the image retrieval. Less

我们对知识获取和系统开发进行了研究，得到了以下结果：(1)我们开发了一种基于支持向量机（SVM）的图像数据分段聚类方法和基于支持向量机集合的特征选择方法。(2)我们开发了一种只保留候选支持向量的增量训练方法，并证明了在删除训练数据的同时保持了泛化能力。(1)动态特征空间学习：我们开发了主成分分析（PCA）和核主成分分析（KPCA）的增量学习算法，并证明了通过适应数据分布的变化成功地进行了特征选择。(2)系统开发：提出了一种将已开发的动态特征选择算法…More ithm集成到分类器模型中的方法，该方法将k近邻方法与动态聚类算法相结合，并结合神经网络模型。我们证明了所提出的方法使分类器能够进行稳定的增量学习，并且开发的系统不仅对基准数据集而且对面部识别数据集都具有出色的性能。虽然我们尝试开发一个增量支持向量机系统，但尚未完成。这是我们今后的工作。(1)图像分割聚类方法的发展：我们开发了一个对不确定目标图像进行检测和分类的基本系统，并证明了利用蛋白质结晶图像数据库对晶体图像进行检测和分类是正确的。(2)图像分割特征提取方法的发展：我们开发了一种基于小波变换的多分辨率光谱直方图的特征提取方法，并证明了所提出的特征可用于图像检索。少