General and Semi-supervised Machine Learning Applied to Bioinformatics

1)人们已经研究了许多不同的方法来对文档集进行聚类，以期提高检索效率。不幸的是，这些通常不能提供改进的检索能力。问题的一部分显然是这样一个事实，即一份特定文件往往涉及一个以上的主题，因此不可能将文件明确地分类为确定的类别，而不包括其他类别。为了克服这一问题，我们开发了旨在从一组文件中确定主题的方法。主题不必涵盖任何文件的全部内容。它只需要存在于文档的某个子集中即可识别。这些文件中的一些文件可能参与了若干主题的定义。一种寻找主题的方法是基于EM算法，并且需要收敛到主题的迭代过程。该方法已被实施和测试，证明是成功的。 2)第二种方法可以基于奇异值分解，本质上是一种向量法。 3)我们还在研究其他方法来提取更高层次的特征。一种有趣的方法是被称为稀疏编码的方法，这是自学学习的基础。

1) Many different methods have been investigated for the purpose of clustering sets of documents with the hope of improving retrieval. Unfortunately these have generally failed to provide improved retrieval capability. Part of the problem is clearly the fact that a given document often involves more than one subject so that it is not possible to make a clean categorization of the documents into definite categories to the exclusion of others. In order to overcome this problem we have developed methods that are designed to identify a theme among a set of documents. The theme need not encompass the whole of any document. It only needs to exist in some subset of the documents in order to be identifiable. Some of these same documents may participate in the definition of several themes. One method of finding themes is based on the EM algorithm and requires an iterative procedure which converges to themes. The method has been implemented and tested and found to be successful. 2) A second approach can be based on the singular value decomposition and essentially is a vector approach. 3) We are also investigating other methods to extract higher level features. One method of interest is the method known as sparse coding, which is the basis of self-taught learning.