Nonlinear relational markov networks with an application to the game of go

Authors:
Tapani Raiko
Affiliations:
Neural Networks Research Centre, Helsinki University of Technology, HUT, Espoo, Finland
Venue:
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
Year:
2005

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Abstract

It would be useful to have a joint probabilistic model for a general relational database. Objects in a database can be related to each other by indices and they are described by a number of discrete and continuous attributes. Many models have been developed for relational discrete data, and for data with nonlinear dependencies between continuous values. This paper combines two of these methods, relational Markov networks and hierarchical nonlinear factor analysis, resulting in joining nonlinear models in a structure determined by the relations in the data. The experiments on collective regression in the board game go suggest that regression accuracy can be improved by taking into account both relations and nonlinearities.