BibSLEIGH — Joao

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: Gama:Jo=atilde=o

Contributed to:

2015

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

1999

1998

1997

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.