In practical classification, there is often a mix of learnable and unlearnable classes and only a classifier above a minimum performance threshold can be deployed. This problem is...
We provide a worst-case analysis of selective sampling algorithms for learning linear threshold functions. The algorithms considered in this paper are Perceptron-like algorithms, ...
Abstract--We participated (as Team 9) in the Article Classification Task of the Biocreative II.5 Challenge: binary classification of fulltext documents relevant for protein-protein...
Automatic text classification is an important operational problem in digital library practice. Most text classification efforts so far concentrated on developing centralized solut...
This paper investigates multi-topic aspects in automatic classification of clinical free text. In many practical situations, we need to deal with documents overlapping with multip...