Knowledge acquisitions from large databases using machine learning techniques

The rapid growth of data in large databases such as text database, scientific database requires efficient computer methods for automating analyses of the data with the goal of acquiring knowledges or making discoveries. Since the analyses of data are generally so expensive, most parts in databases remains as raw, unanalyzed, primary data. Technology from machine learning theory will offer efficient tools for the intelligent analysis using "generalization" ability. Generalization is an important ability specific to inductive learning which will predict unseen data with high accuracy based on learned concepts from training examples. We will demonstrate the effectiveness of our approach where generalization ability is applied to predicting and analyzing primary data and extracting knowledges from database by presenting some our results on text database analysis and biological sequence analysis.