Statistics Gathering for Learning from Distributed, Heterogeneous and Autonomous Data Sources

With the growing use of distributed information networks, there is an increasing need for algorithmic and system solutions for data-driven knowledge acquisition using distributed, heterogeneous and autonomous data repositories. In many applications, practical constraints require such systems to provide support for data analysis where the data and the computational resources are available. This presents us with distributed learning problems. We precisely formulate a class of distributed learning problems; present a general strategy for transforming traditional machine learning algorithms into distributed learning algorithms based on the decomposition of the learning task into hypothesis generation and information extraction components; formally defined the information required for generating the hypothesis (sufficient statistics); and show how to gather the sufficient statistics from distributed, heterogeneous, autonomous data sources, using a query decomposition (planning) approach...

• Type: Conference
• Year: 2003
• Where: IJCAI
• Authors: Doina Caragea, Jaime Reinoso, Adrian Silvescu, Vasant Honavar
• Keywords: Autonomous Data, Distributed Learning, IJCAI 2003, IJCAI 2007, Learning Algorithms