REPORT Pronobis_Idiap-RR-08-2010/IDIAP The More you Learn, the Less you Store: Memory-controlled Incremental SVM for Visual Place Recognition Pronobis, Andrzej Luo, Jie Caputo, Barbara EXTERNAL http://publications.idiap.ch/attachments/reports/2009/Pronobis_Idiap-RR-08-2010.pdf PUBLIC http://publications.idiap.ch/index.php/publications/showcite/Pronobis_IMAVIS_2010 Related documents Idiap-RR-08-2010 2010 Idiap April 2010 The capability to learn from experience is a key property for autonomous cognitive systems working in realistic settings. To this end, this paper presents an SVM- -based algorithm, capable of learning model representations incrementally while keeping under control memory requirements. We combine an incremental extension of SVMs [45] with a method reducing the number of support vectors needed to build the decision function without any loss in performance [15] introducing a parameter which permits a user-set trade-off between performance and memory. The resulting algorithm is able to achieve the same recognition results as the original incremental method while reducing the memory growth. Our method is especially suited to work for autonomous systems in realistic settings. We present experiments on two common scenarios in this domain: adaptation in presence of dynamic changes and transfer of knowledge between two different autonomous agents, focusing in both cases on the problem of visual place recognition applied to mobile robot topological localization. Experiments in both scenarios clearly show the power of our approach. ARTICLE Pronobis_IMAVIS_2010/IDIAP The More you Learn, the Less you Store: Memory-controlled Incremental SVM for Visual Place Recognition Pronobis, Andrzej Luo, Jie Caputo, Barbara EXTERNAL http://publications.idiap.ch/attachments/papers/2010/Pronobis_IMAVIS_2010.pdf PUBLIC Image and Vision Computing 2010 February 2010 http://dx.doi.org/10.1016/j.imavis.2010.01.015 doi The capability to learn from experience is a key property for autonomous cognitive systems working in realistic settings. To this end, this paper presents an SVM-based algorithm, capable of learning model representations incrementally while keeping under control memory requirements. We combine an incremental extension of SVMs with a method reducing the number of support vectors needed to build the decision function without any loss in performance introducing a parameter which permits a user-set trade-off between performance and memory. The resulting algorithm is able to achieve the same recognition results as the original incremental method while reducing the memory growth. Our method is especially suited to work for autonomous systems in realistic settings. We present experiments on two common scenarios in this domain: adaptation in presence of dynamic changes and transfer of knowledge between two different autonomous agents, focusing in both cases on the problem of visual place recognition applied to mobile robot topological localization. Experiments in both scenarios clearly show the power of our approach.