Treffer: Heterogeneous modeling for statistical learning with Bayesian nonparametric approaches =: 變參貝葉斯方法在統計學習異構分析中的應用 ; 變參貝葉斯方法在統計學習異構分析中的應用 ; Heterogeneous modeling for statistical learning with Bayesian nonparametric approaches =: Bian can Beiyesi fang fa zai tong ji xue xi yi gou fen xi zhong de ying yong ; Bian can Beiyesi fang fa zai tong ji xue xi yi gou fen xi zhong de ying yong

Ph.D. ; Recently have witnessed the increasing interest and popularity on developing machine learning theories, algorithms and applications in the community of computer science. Among different machine learning methods, the statistical learning approach is a very promising direction, which aims to deal with the problem of finding predictive models based on given data samples by modeling them with certain probability distributions. It has found many different real-world applications in various areas including social computing, data mining, natural language processing, computer vision and signal processing. Within these empirical applications, it is common to observe differences across data points on various statistical features, or the statistical heterogeneity. However, many of them have been omitted in previous approaches. ; This thesis aims to address the issue of handling the heterogeneous statistics among data in empirical machine learning problems, by exploring the heterogeneous properties among data samples from different perspectives. The basic idea is to group data samples based on different features according to empirical requirement, and the heterogeneous properties can be well captured accordingly. However, directly grouping data points can be nontrivial empirically. Because in some practical scenarios, the data points are heterogeneous with respect to latent perspectives. To tackle this issue, we proposed methods exploring the latent features and perform grouping simultaneously with an iterative statistical inference scheme. Another vital issue is that, how to determine the number of groups for data points. We further propose the Bayesian nonparametric approaches to handle this question effectively. ; This thesis will be divided into seven parts, which are organized as following. The first part will provide a brief introduction on previous works on statistical learning with its applications, and the background knowledge on heterogeneous modeling. The second and third parts will introduce the ...