2008
Autores
Ikonotnovska, E; Gama, J;
Publicação
DISCOVERY SCIENCE, PROCEEDINGS
Abstract
In this paper we propose a fast and incremental algorithm for learning model trees from data streams (FIMT) for regression problems. The algorithm is incremental, works online, processes examples once at the speed they arrive, and maintains an any-time regression model. The leaves contain linear-models trained online from the examples that fall at that leaf, a process with low complexity. The use of linear models in the leaves increases its any-time global performance. FIMT is able to obtain competitive accuracy with batch learners even for medium size datasets, but with better training time in an order of magnitude. We study the properties of FIMT over several artificial and real datasets and evaluate its sensitivity on the order of examples and the noise level.
2010
Autores
Gaber, MM; Vatsavai, RR; Omitaomu, OA; Gama, J; Chawla, NV; Ganguly, AR;
Publicação
KDD Workshop on Knowledge Discovery from Sensor Data
Abstract
2011
Autores
Gama, J; Bradley, E; Hollmén, J;
Publicação
IDA
Abstract
2009
Autores
Huang, R; Yang, Q; Pei, J; Gama, J; Meng, X; Li, X;
Publicação
ADMA
Abstract
2010
Autores
Gama, J; Rodrigues, PP; Spinosa, EJ; Carvalho, ACPLFd;
Publicação
Web Intelligence and Security - Advances in Data and Text Mining Techniques for Detecting and Preventing Terrorist Activities on the Web
Abstract
2009
Autores
Gama, J; Rodrigues, PP;
Publicação
Encyclopedia of Data Warehousing and Mining, Second Edition (4 Volumes)
Abstract
The access to the final selection minute is only available to applicants.
Please check the confirmation e-mail of your application to obtain the access code.