This paper presents a supervised machine learning approach for summarizing legal documents. A commercial system for the analysis and summarization of legal documents provided us wi...
Learning Bayesian Belief Networks (BBN) from corpora and incorporating the extracted inferring knowledge with a Support Vector Machines (SVM) classifier has been applied to charac...
In this paper we propose a schema and framework for recording and managing attention metadata. This framework is intended to capture, manage, and re-use data about attention users...
Background: Machine-learning tools have gained considerable attention during the last few years for analyzing biological networks for protein function prediction. Kernel methods a...
Conditional Random Sampling (CRS) was originally proposed for efficiently computing pairwise (l2, l1) distances, in static, large-scale, and sparse data. This study modifies the o...