Neural Comput - Online learning of single- and multivalued functions with an infinite mixture of linear experts.

Tópicos

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Resumo

We present a supervised learning algorithm for estimation of generic input-output relations in a real-time, online fashion. The proposed method is based on a generalized expectation-maximization approach to fit an infinite mixture of linear experts (IMLE) to an online stream of data samples. This probabilistic model, while not fully Bayesian, can efficiently choose the number of experts that are allocated to the mixture, this way effectively controlling the complexity of the resulting model. The result is an incremental, online, and localized learning algorithm that performs nonlinear, multivariate regression on multivariate outputs by approximating the target function by a linear relation within each expert input domain and that can allocate new experts as needed. A distinctive feature of the proposed method is the ability to learn multivalued functions: one-to-many mappings that naturally arise in some robotic and computer vision learning domains, using an approach based on a Bayesian generative model for the predictions provided by each of the mixture experts. As a consequence, it is able to directly provide forward and inverse relations from the same learned mixture model. We conduct an extensive set of experiments to evaluate the proposed algorithm performance, and the results show that it can outperform state-of-the-art online function approximation algorithms in single-valued regression, while demonstrating good estimation capabilities in a multivalued function approximation context.

Resumo Limpo

present supervis learn algorithm estim generic inputoutput relat realtim onlin fashion propos method base general expectationmaxim approach fit infinit mixtur linear expert iml onlin stream data sampl probabilist model fulli bayesian can effici choos number expert alloc mixtur way effect control complex result model result increment onlin local learn algorithm perform nonlinear multivari regress multivari output approxim target function linear relat within expert input domain can alloc new expert need distinct featur propos method abil learn multivalu function onetomani map natur aris robot comput vision learn domain use approach base bayesian generat model predict provid mixtur expert consequ abl direct provid forward invers relat learn mixtur model conduct extens set experi evalu propos algorithm perform result show can outperform stateoftheart onlin function approxim algorithm singlevalu regress demonstr good estim capabl multivalu function approxim context

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