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Publicações

Publicações por João Gama

2008

Learning Model Trees from Data Streams

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

Knowledge Discovery from Sensor Data, Second International Workshop, Sensor-KDD 2008, Las Vegas, NV, USA, August 24-27, 2008, Revised Selected Papers

Autores
Gaber, MM; Vatsavai, RR; Omitaomu, OA; Gama, J; Chawla, NV; Ganguly, AR;

Publicação
KDD Workshop on Knowledge Discovery from Sensor Data

Abstract

2011

Advances in Intelligent Data Analysis X - 10th International Symposium, IDA 2011, Porto, Portugal, October 29-31, 2011. Proceedings

Autores
Gama, J; Bradley, E; Hollmén, J;

Publicação
IDA

Abstract

2009

Advanced Data Mining and Applications, 5th International Conference, ADMA 2009, Beijing, China, August 17-19, 2009. Proceedings

Autores
Huang, R; Yang, Q; Pei, J; Gama, J; Meng, X; Li, X;

Publicação
ADMA

Abstract

2010

Knowledge Discovery from Data Streams

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

Data Streams

Autores
Gama, J; Rodrigues, PP;

Publicação
Encyclopedia of Data Warehousing and Mining, Second Edition (4 Volumes)

Abstract

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