Abstract We introduce a theoretical framework for understanding and predicting the complexity of sequence classification tasks, using novel extension theory Boolean function sensitivity. The sensitivity function, given distribution over input sequences, quantifies number disjoint subsets that can each be individually changed to change output. argue standard methods are biased towards learning l...

Abstract Complexity measures aim to characterize the underlying complexity of supervised data. These tackle factors hindering performance Machine Learning ( ML ) classifiers like overlap, density, linearity, etc. The state-of-the-art has mainly focused on dataset perspective complexity, i.e., offering an estimation whole dataset. Recently, instance also been addressed. In this paper, hostility ...

Abstract. Transductive learning considers situations when a learner observes m labelled training points and u unlabelled test points with the final goal of giving correct answers for the test points. This paper introduces a new complexity measure for transductive learning called Permutational Rademacher Complexity (PRC) and studies its properties. A novel symmetrization inequality is proved, wh...