Multiple Instance Learning (MIL) is a special kind of supervised learning problem that has been studied actively in recent years. We propose an approach based on One-Class Support ...
Abstract. The inefficiency of integration processes—as an abstraction of workflow-based integration tasks—is often reasoned by low resource utilization and significant waiti...
In this paper, we develop an efficient logistic regression model for multiple instance learning that combines L1 and L2 regularisation techniques. An L1 regularised logistic regr...
We present a method for explaining predictions for individual instances. The presented approach is general and can be used with all classification models that output probabilities...
Abstract. Changes in the characterization of instances in digital content are one of the rationales to evolve ontologies that support a domain. These changes can have impacts on on...
Yalemisew M. Abgaz, Muhammad Javed 0002, Claus Pah...