Learning temporal structure for task based control
Image and Vision Computing
Learning Object Representations Using Sequential Patterns
AI '08 Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
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We present an example of a joint spatial and temporal task learning algorithm that results in a generative model that has applications for on-line visual control. We review work on learning transformed mixture of gaussians (due to Frey and Jojic) and Variable Length Markov Models (VLMMS due to Ron, Singer and Tishby). We show how a temporal model, learned through an extension of VLMMs to deal with multinomially distributed input symbol vectors, can be used as an improvement on Maximum Likelihood (ML) for prior parameter estimation for the Expectation Maximisation (EM) process.