Stem drift$ (all stems)

46 papers:

DATE-2015-KanounS #big data #concept #data type #detection #learning #online #scheduling #streaming

Big-data streaming applications scheduling with online learning and concept drift detection (KK, MvdS), pp. 1547–1550.

ICEIS-v1-2015-BurtiniLL #multi #online

Improving Online Marketing Experiments with Drifting Multi-armed Bandits (GB, JL, RL), pp. 630–636.

ECIR-2015-FeiHY #microblog #topic

Handling Topic Drift for Topic Tracking in Microblogs (YF, YH, JY), pp. 477–488.

ICML-2015-GuanSBMBB #linear

Moderated and Drifting Linear Dynamical Systems (JG, KS, EB, CM, EB, KB), pp. 2473–2482.

HCI-AIMT-2014-LifOHALP #evaluation

Evaluation of Tactile Drift Displays in Helicopter (PL, PAO, JH, PA, BL, CP), pp. 578–588.

ICML-c2-2014-HarelMEC #concept #detection

Concept Drift Detection Through Resampling (MH, SM, REY, KC), pp. 1009–1017.

ICPR-2014-TsukiokaK

Selection of Features in Accord with Population Drift (HT, MK), pp. 1591–1596.

KDD-2014-ShaoAK #concept #data type #learning #prototype

Prototype-based learning on concept-drifting data streams (JS, ZA, SK), pp. 412–421.

MLDM-2014-WaiyamaiKSR #classification #concept #data type #named

ACCD: Associative Classification over Concept-Drifting Data Streams (KW, TK, BS, TR), pp. 78–90.

SAC-2014-BarddalGE #classification #concept #named #network #social

SFNClassifier: a scale-free social network method to handle concept drift (JPB, HMG, FE), pp. 786–791.

CASE-2013-MarturiDP #image #performance #using

Fast image drift compensation in scanning electron microscope using image registration (NM, SD, NP), pp. 807–812.

CIKM-2013-AlbakourMO #effectiveness #microblog #on the #realtime

On sparsity and drift for effective real-time filtering in microblogs (MDA, CM, IO), pp. 419–428.

DAC-2012-KimYL #latency #performance #ram

Write performance improvement by hiding R drift latency in phase-change RAM (YK, SY, SL), pp. 897–906.

ICPR-2012-TurkovKM #concept #pattern matching #pattern recognition #problem #recognition

The Bayesian logistic regression in pattern recognition problems under concept drift (PAT, OK, VM), pp. 2976–2979.

MLDM-2012-TurkovKM #approach #concept #pattern matching #pattern recognition #problem #recognition

Bayesian Approach to the Concept Drift in the Pattern Recognition Problems (PAT, OK, VM), pp. 1–10.

CAiSE-2011-BoseAZP #concept #mining #process

Handling Concept Drift in Process Mining (RPJCB, WMPvdA, IZ, MP), pp. 391–405.

CIKM-2010-SotoudehA #detection #framework #induction #using

Partial drift detection using a rule induction framework (DS, AA), pp. 769–778.

CIKM-2010-ZhangZTG #concept #data type #framework #named

SKIF: a data imputation framework for concept drifting data streams (PZ, XZ, JT, LG), pp. 1869–1872.

KDIR-2010-WangSFR #concept

A Meta-learning Method for Concept Drift (RW, LS, MÓF, ER), pp. 257–262.

DATE-2009-ArmengaudS #metric #network

Remote measurement of local oscillator drifts in FlexRay networks (EA, AS), pp. 1082–1087.

MSR-2009-EkanayakeTGB #concept #fault #predict #quality #using

Tracking concept drift of software projects using defect prediction quality (JE, JT, HCG, AB), pp. 51–60.

CIKM-2009-CaoCYX #recommendation

Enhancing recommender systems under volatile userinterest drifts (HC, EC, JY, HX), pp. 1257–1266.

ICML-2009-Freund #game studies #learning #online

Invited talk: Drifting games, boosting and online learning (YF), p. 2.

KDD-2009-GuptaBR #learning

Catching the drift: learning broad matches from clickthrough data (SG, MB, MR), pp. 1165–1174.

MLDM-2009-LiHLG #concept #detection #random #streaming

Concept Drifting Detection on Noisy Streaming Data in Random Ensemble Decision Trees (PPL, XH, QL, YG), pp. 236–250.

MLDM-2009-RosenthalVHHL

Drift-Aware Ensemble Regression (FR, PBV, MH, DH, WL), pp. 221–235.

SAC-2009-FachadaLR #simulation

Simulating antigenic drift and shift in influenza A (NF, VVL, ACR), pp. 2093–2100.

CASE-2008-ScheitererNOSG #performance #precise #protocol

Synchronization performance of the Precision Time Protocol in the face of slave clock frequency drift (RLS, CN, DO, GS, DG), pp. 554–559.

DAC-2008-MohalikRDRSPJ #analysis #embedded #latency #model checking #realtime

Model checking based analysis of end-to-end latency in embedded, real-time systems with clock drifts (SM, ACR, MGD, SR, PVS, PKP, SJ), pp. 296–299.

ICPR-2008-KarnickMP #approach #classification #concept #incremental #learning #multi #using

Incremental learning in non-stationary environments with concept drift using a multiple classifier based approach (MTK, MM, RP), pp. 1–4.

KDD-2008-ZhangZS #categorisation #concept #data type #mining

Categorizing and mining concept drifting data streams (PZ, XZ, YS), pp. 812–820.

SIGIR-2008-ZighelnicK #query #robust

Query-drift prevention for robust query expansion (LZ, OK), pp. 825–826.

CIKM-2007-MacdonaldO #query

Expertise drift and query expansion in expert search (CM, IO), pp. 341–350.

KDD-2007-BeckerA #concept #ranking #realtime #using

Real-time ranking with concept drift using expert advice (HB, MA), pp. 86–94.

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.

KDD-2006-WangYPYY #concept #data type #mining

Suppressing model overfitting in mining concept-drifting data streams (HW, JY, JP, PSY, JXY), pp. 736–741.

SIGIR-2006-Forman #concept #induction

Tackling concept drift by temporal inductive transfer (GF), pp. 252–259.

ICML-2005-KolterM #concept #using

Using additive expert ensembles to cope with concept drift (JZK, MAM), pp. 449–456.

VLDB-2004-Fan #classification #concept #data type #named

StreamMiner: A Classifier Ensemble-based Engine to Mine Concept-drifting Data Streams (WF), pp. 1257–1260.

KDD-2004-Fan #concept #data type

Systematic data selection to mine concept-drifting data streams (WF), pp. 128–137.

KDD-2003-PrattT #concept #visualisation

Visualizing concept drift (KBP, GT), pp. 735–740.

KDD-2003-WangFYH #classification #concept #data type #mining #using

Mining concept-drifting data streams using ensemble classifiers (HW, WF, PSY, JH), pp. 226–235.

ICML-2000-KlinkenbergJ #concept #detection

Detecting Concept Drift with Support Vector Machines (RK, TJ), pp. 487–494.

KDD-1999-SyedLS99a #concept #incremental #learning

Handling Concept Drifts in Incremental Learning with Support Vector Machines (NAS, HL, KKS), pp. 317–321.

KDD-1998-LaneB #concept #identification #learning #online #security

Approaches to Online Learning and Concept Drift for User Identification in Computer Security (TL, CEB), pp. 259–263.