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We analyse optimal rejection strategies for classifiers with input space partitioning, e.g. prototype-based classifiers, support vector machines or decision trees using certainty measures such as the distance to the closest decision border. We provide new theoretical results: we link this problem to the multiple choice knapsack problem and devise an exact polynomial-time dynamic programming (DP) scheme...
One of the main aims of lifelong learning architectures is to efficiently and reliably cope with the stability-plasticity dilemma. A viable solution of this dilemma combines a static offline classifier, which preserves ground knowledge that should be respected during training, with an incremental online learning of new or specific information encountered during use. A feasible realisation has been...
One of the greatest challenges of life-long learning architectures is how to efficiently and reliably cope with the stability-plasticity dilemma. We propose an extension of a flexible system combining a static offline classifier and an incremental online classifier that is well suited for life-long learning scenarios. The pre-trained offline classifier preserves ground knowledge that should be respected...
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