Jun Lei
ABSTRACT
Aiming at the large memory footprint of traditional vector machine (Relevance Vector Machine, RVM) when processing big data in supervised learning, the idea of incremental learning is introduced into the traditional RVM and the incremental learning of RVM based on sparse model is studied. Method of an incremental learning algorithm of RVM based on sparse model is proposed. The algorithm considers the influence of the existing model and the new sample on the sparse RVM model, and transforms each incremental learning to the problem of solving the maximized edge likelihood function. The sparse RVM model is updated by solving the optimization problem continuously. Simulation results show that this method can effectively reduce the memory space requirements.
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Index Terms
- Incremental Learning Algorithm Based on Relevance Vector Machine
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