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Travelled to:
1 × Australia
1 × Brazil
1 × Cyprus
1 × Germany
1 × Italy
1 × Korea
1 × Portugal
1 × Slovenia
1 × Taiwan
1 × United Kingdom
2 × France
2 × Spain
7 × USA
Collaborated with:
P.P.Rodrigues C.Pinto P.Medas P.Kosina R.Sarmento M.Cordeiro R.Sebastião E.J.Spinosa R.Rocha E.R.Faria A.C.P.L.F.Carvalho E.Ikonomovska S.Dzeroski J.Vinagre A.M.Jorge V.Cerqueira M.D.B.Oliveira E.Almeida S.Krishnaswamy M.M.Gaber H.Kargupta W.Fan A.C.P.L.F.d.Carvalho A.C.P.d.L.F.d.Carvalho L.Moreira-Matias J.Mendes-Moreira P.Brazdil R.C.Barros B.Zenko J.Araújo L.M.B.Lopes P.Matuszyk M.Spiliopoulou
Talks about:
data (15) stream (14) tree (8) network (5) cluster (5) base (5) algorithm (4) detect (4) mine (4) increment (3)

Person: João Gama

DBLP DBLP: Gama:Jo=atilde=o

Contributed to:

ICEIS v1 20152015
SAC 20152015
SAC 20132013
MLDM 20122012
SAC 20122012
CIKM 20112011
ICML 20112011
SAC 20112011
KDD 20102010
KDD 20092009
SAC 20092009
SAC 20082008
SAC 20072007
SAC 20062006
SAC 20052005
SAC 20042004
KDD 20032003
ICML 20022002
ICML 19991999
ICML 19981998
ICML 19971997

Wrote 28 papers:

ICEIS-v1-2015-CerqueiraOG #community #framework #network #scalability #social
A Framework for Analysing Dynamic Communities in Large-scale Social Networks (VC, MDBO, JG), pp. 235–242.
ICEIS-v1-2015-SarmentoCG #network #streaming #using
Streaming Networks Sampling using top-K Networks (RS, MC, JG), pp. 228–234.
SAC-2015-MatuszykVSJG #incremental #matrix #recommendation
Forgetting methods for incremental matrix factorization in recommender systems (PM, JV, MS, AMJ, JG), pp. 947–953.
SAC-2015-SarmentoCG #evolution #scalability #visualisation
Visualization of evolving large scale ego-networks (RS, MC, JG), pp. 960–962.
SAC-2015-VinagreJG #collaboration #feedback
Collaborative filtering with recency-based negative feedback (JV, AMJ, JG), pp. 963–965.
SAC-2013-AlmeidaKG #data type #random
Random rules from data streams (EA, PK, JG), pp. 813–814.
SAC-2013-FariaGC #algorithm #data type #detection #multi #problem
Novelty detection algorithm for data streams multi-class problems (ERF, JG, ACPLFC), pp. 795–800.
MLDM-2012-Moreira-MatiasMGB #categorisation #classification #matrix #using
Text Categorization Using an Ensemble Classifier Based on a Mean Co-association Matrix (LMM, JMM, JG, PB), pp. 525–539.
SAC-2012-FariaBGC #algorithm #clustering #data type
Improving the offline clustering stage of data stream algorithms in scenarios with variable number of clusters (ERF, RCB, JG, ACPLFC), pp. 829–830.
SAC-2012-KosinaG #multi #performance #problem
Very Fast Decision Rules for multi-class problems (PK, JG), pp. 795–800.
CIKM-2011-KrishnaswamyGG #data type #mining #mobile #roadmap #ubiquitous
Advances in data stream mining for mobile and ubiquitous environments (SK, JG, MMG), pp. 2607–2608.
ICML-2011-IkonomovskaGZD
Speeding-Up Hoeffding-Based Regression Trees With Options (EI, JG, BZ, SD), pp. 537–544.
SAC-2011-IkonomovskaGD #data type #incremental #multi
Incremental multi-target model trees for data streams (EI, JG, SD), pp. 988–993.
SAC-2011-RodriguesGAL #clustering #named
L2GClust: local-to-global clustering of stream sources (PPR, JG, JA, LMBL), pp. 1006–1011.
KDD-2010-KarguptaGF #data mining #generative #mining
The next generation of transportation systems, greenhouse emissions, and data mining (HK, JG, WF), pp. 1209–1212.
KDD-2009-GamaSR #algorithm #evaluation #learning
Issues in evaluation of stream learning algorithms (JG, RS, PPR), pp. 329–338.
SAC-2009-GamaRS #algorithm #data type
Evaluating algorithms that learn from data streams (JG, PPR, RS), pp. 1496–1500.
SAC-2008-SpinosaCG #clustering #concept #data type #detection #network #novel
Cluster-based novel concept detection in data streams applied to intrusion detection in computer networks (EJS, ACPLFdC, JG), pp. 976–980.
SAC-2007-PintoG #concept #incremental
Incremental discretization, application to data with concept drift (CP, JG), pp. 467–468.
SAC-2007-SpinosaCG #approach #clustering #concept #data type #detection #named
OLINDDA: a cluster-based approach for detecting novelty and concept drift in data streams (EJS, ACPdLFdC, JG), pp. 448–452.
SAC-2006-GamaP #data mining #data type #mining
Discretization from data streams: applications to histograms and data mining (JG, CP), pp. 662–667.
SAC-2005-GamaMR #data type #learning
Learning decision trees from dynamic data streams (JG, PM, PPR), pp. 573–577.
SAC-2004-GamaMR #online
Forest trees for on-line data (JG, PM, RR), pp. 632–636.
KDD-2003-GamaRM #data type #mining #performance
Accurate decision trees for mining high-speed data streams (JG, RR, PM), pp. 523–528.
ICML-2002-Gama #analysis #functional
An Analysis of Functional Trees (JG), pp. 155–162.
ICML-1999-Gama
Discriminant Trees (JG), pp. 134–142.
ICML-1998-Gama
Local Cascade Generalization (JG), pp. 206–214.
ICML-1997-Gama #linear #probability
Probabilistic Linear Tree (JG), pp. 134–142.

Bibliography of Software Language Engineering in Generated Hypertext (BibSLEIGH) is created and maintained by Dr. Vadim Zaytsev.
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