Statistical models for spatial multivariate date and discriminate analysis

本研究的主要目的是推导出一个有效的分类程序的基础上地理空间数据的土地覆盖类别。假设特征向量服从正态分布，类别分布服从结构马尔可夫随机场（MRF），得到了一种分类方法，该方法对真实的数据具有良好的分类性能。然后，我们继续研究一般的结构MRF和未知参数的估计方法。这些结果在两个国际会议上作为特邀演讲（提交出版）。结果表明，MRF模型的空间依赖的类别。接下来，我们对特征空间采用机器学习方法。对概率支持向量机（SVM）进行了处理，用损失函数定义了后验概率。由于统计方法，这种方法给出了类似的效率。然而，这是一个改进后部的空间。这一点目前正在研究中。另一个关注点是人类基因的微阵列数据。其目的是通过机器学习根据基因表达模式对细胞类型进行分类。首先，AdaBoost从数千个基因中选择数十个基因。然后，SVM选择所选基因的最佳组合。我们的方法比普通方法（准备中）效率更高。

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).