Several pattern classifiers give high classification accuracy but their storage requirements and processing time are severely expensive. On the other hand, some classifiers requir...
Alok Sharma, Kuldip K. Paliwal, Godfrey C. Onwubol...
This paper describes a method to minimize the immense training time of the conventional Adaboost learning algorithm in object detection by reducing the sampling area. A new algorit...
Florian Baumann, Katharina Ernst, Arne Ehlers, Bod...
For many NLP tasks, including named entity tagging, semi-supervised learning has been proposed as a reasonable alternative to methods that require annotating large amounts of trai...
We investigate ways in which an algorithm can improve its expected performance by fine-tuning itself automatically with respect to an arbitrary, unknown input distribution. We gi...
Nir Ailon, Bernard Chazelle, Kenneth L. Clarkson, ...
A statistical framework for modeling and prediction of binary matrices is presented. The method is applied to social network analysis, specifically the database of US Supreme Cou...
Eric Wang, Jorge Silva, Rebecca Willett, Lawrence ...