An Information Reinstatement Dealing with Machine Learning

Abstract

Information retrieval using probabilistic techniques has attracted significant attention on the part of researchers in information and computer science over the past few decades. The process of machine learning is similar to that of data mining. Both systems search through data to look for patterns. Machine learning programs detect patterns in data and adjust program actions accordingly.  In this paper,  we are exploring the use of machine learning techniques for information retrieval and we are using machine learning algorithms that can benefit from limited training data in order to identify a ranker likely to achieve high retrieval performance over unseen documents and queries. This problem presents novel challenges compared to traditional learning tasks, such as regression or classification. We are investigating the discriminative learning of ad-hoc retrieval models. For that purpose, we propose different models based on kernel machines or neural networks adapted to different retrieval contexts. The proposed approaches rely on different online learning algorithms that allow efficient learning over large collection and finally approaches rely on discriminative learning and enjoy efficient training procedures, which yields effective and scalable models.