General and Semi-supervised Machine Learning Applied to Bioinformatics

1)许多不同的方法已被调查的目的，聚类集的文件，希望提高检索。不幸的是，这些通常不能提供改进的检索能力。部分问题显然是，一份文件往往涉及一个以上的主题，因此不可能将文件明确归类为明确的类别，而排除其他类别。为了克服这个问题，我们已经开发出的方法，旨在确定一组文件中的主题。主题不必涵盖任何文件的全部。它只需要存在于文档的某个子集中即可识别。其中一些文件可能参与若干主题的界定。发现主题的一种方法是基于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，并发现是成功的。 2)第二种方法可以基于奇异值分解，本质上是一种向量方法。 3)我们还在研究其他方法来提取更高级别的特征。感兴趣的一种方法是称为稀疏编码的方法，这是自学的基础。

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.