Statistical models for spatial multivariate date and discriminate analysis

空间多元数据和判别分析的统计模型

基本信息

批准号：
13640117
负责人：
NISHII Ryuei
金额：
$ 2.05万
依托单位：
HIROSHIMA UNIVERSITY
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
2001
资助国家：
日本
起止时间：
2001 至 2002
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-13640117/
关键词：
AdaBoost Image segmentation MAP estimate Markov random filed Spatial dependency サポートベクターマシン AdaBoost image segmentation MAP estimate Markov random field Spatial dependency Support vector machine Data fusion ICM MAP estmate

项目摘要

The main aim of this study was to derive an efficient classification procedure for land-cover categories based on geospatial date. Assuming normal distributions for feature vectors and a structural Markov random field (MRF) for category distribution, we obtained a classification method, which shows an excellent performance for real date. Then, we proceeded to examine general structural MRF and an estimation method of unknown parameters. These results were presented as invited talks at two international conferences (submitted for publication).It was shown that MRF models the spatial dependency of the categories well. Next, we take machine-learning approach for feature space. Probabilistic support vector machine (SVM ) is treated and the posterior probability is defined by the loss function. This approach gives a similar efficiency due to the statistical approach. These is, however, a room for improvement of the posterior. This point is now under investigation.Another attention was paid for microarray data on human gene. The aim is to classify cell types by gene expression patterns through machine learning. First, dozens of genes are selected among several thousands genes by AdaBoost. Then, SVM chooses the best combination of the selected genes. Our approach is highly efficient than the ordinary (in preparation).

这项研究的主要目的是根据地理空间日期来得出土地覆盖类别的有效分类程序。假设特征向量的正常分布和类别分布的结构马尔可夫随机场（MRF），我们获得了一种分类方法，该方法显示了实际日期的出色性能。然后，我们开始检查一般的结构MRF和一种未知参数的估计方法。这些结果作为在两次国际会议上的受邀演讲（已提交出版）表示。接下来，我们采用机器学习方法的特征空间。处理概率支持向量机（SVM），后验概率由损耗函数定义。由于统计方法，这种方法具有类似的效率。但是，这是改善后部的空间。这一点现在正在研究中。其他关注人类基因的微阵列数据。目的是通过机器学习通过基因表达模式对细胞类型进行分类。首先，在adaboost中选择了数十个基因。然后，SVM选择选定基因的最佳组合。我们的方法比普通（准备）高效。