Abstract. We present a new local strategy to solve incremental learning tasks. Applied to Support Vector Machines based on local kernel, it allows to avoid re-learning of all the parameters by selecting a working subset where the incremental learning is performed. Automatic selection procedure is based on the estimation of generalization error by using theoretical bounds that involve the margin notion. Experimental simulation on three typical datasets of machine learning give promising results.
Dalché-Buc and Ralaivola REF presented a new local strategy to solve incremental learning tasks.
Incremental Learning Algorithms for Classification and Regression: local strategies
AIP Conference Proceedings
AIP Conference Proceedings
Computer Science