In traditional text classification, a classifier is built using labeled training documents of every class. This paper studies a different problem. Given a set P of documents of a ...
Abstract— This paper discusses the generation of informationrich, arbitrarily-large synthetic data sets which can be used to (a) efficiently learn tests that correlate a set of ...
Haralampos-G. D. Stratigopoulos, Salvador Mir, Yio...
We study metric learning as a problem of information retrieval. We present a general metric learning algorithm, based on the structural SVM framework, to learn a metric such that ...
Visualization techniques provide an outstanding role in KDD process for data analysis and mining. However, one image does not always convey successfully the inherent information fr...
This paper is an empirical investigation into the effectiveness of linear scaling adaptation for case-based software project effort prediction. We compare two variants of a linea...
Colin Kirsopp, Emilia Mendes, Rahul Premraj, Marti...