Stem ensembl$ (all stems)

199 papers:

SCSM-2015-Kanawati #community #detection #network

Ensemble Selection for Community Detection in Complex Networks (RK), pp. 138–147.

ICEIS-v1-2015-SouzaBGBE #learning #online

Applying Ensemble-based Online Learning Techniques on Crime Forecasting (AJdS, APB, HMG, JPB, FE), pp. 17–24.

ECIR-2015-HagenPBS #classification #detection #sentiment #twitter #using

Twitter Sentiment Detection via Ensemble Classification Using Averaged Confidence Scores (MH, MP, MB, BS), pp. 741–754.

KDD-2015-LiuLWTF #clustering

Spectral Ensemble Clustering (HL, TL, JW, DT, YF), pp. 715–724.

SIGIR-2015-ChenLZLS #approximate #matrix #named #recommendation #scalability

WEMAREC: Accurate and Scalable Recommendation through Weighted and Ensemble Matrix Approximation (CC, DL, YZ, QL, LS), pp. 303–312.

SIGIR-2015-LuccheseNOPTV #algorithm #documentation #named #performance #rank

QuickScorer: A Fast Algorithm to Rank Documents with Additive Ensembles of Regression Trees (CL, FMN, SO, RP, NT, RV), pp. 73–82.

SAC-2015-GomesBE #classification #data type #learning

Pairwise combination of classifiers for ensemble learning on data streams (HMG, JPB, FE), pp. 941–946.

SAC-2015-StracciaM #concept #estimation #fuzzy #learning #named #owl #probability #using

pFOIL-DL: learning (fuzzy) EL concept descriptions from crisp OWL data using a probabilistic ensemble estimation (US, MM), pp. 345–352.

SAC-2015-TambeN #behaviour #game studies #modelling #resource management #robust #security

Robust resource allocation in security games and ensemble modeling of adversary behavior (AT, TN), pp. 277–282.

DocEng-2014-NourashrafeddinMA #approach #clustering #concept #documentation #using #wiki

An ensemble approach for text document clustering using Wikipedia concepts (SN, EEM, DVA), pp. 107–116.

CHI-2014-MartinGS #gesture

Exploring percussive gesture on iPads with ensemble metatone (CM, HJG, BS), pp. 1025–1028.

CSCW-2014-KimCB #collaboration #named

Ensemble: exploring complementary strengths of leaders and crowds in creative collaboration (JK, JC, MSB), pp. 745–755.

CIKM-2014-LinLYC #classification #modelling #sentiment

Exploring Ensemble of Models in Taxonomy-based Cross-Domain Sentiment Classification (CKL, YYL, CHY, HHC), pp. 1279–1288.

ECIR-2014-Lommatzsch #realtime #recommendation #using

Real-Time News Recommendation Using Context-Aware Ensembles (AL), pp. 51–62.

ICML-c1-2014-LacosteMLL #learning

Agnostic Bayesian Learning of Ensembles (AL, MM, FL, HL), pp. 611–619.

ICML-c2-2014-BaiLS #classification #framework #online

A Bayesian Framework for Online Classifier Ensemble (QB, HL, SS), pp. 1584–1592.

ICML-c2-2014-CortesKM #predict

Ensemble Methods for Structured Prediction (CC, VK, MM), pp. 1134–1142.

ICML-c2-2014-WangY #crowdsourcing

Ensemble-Based Tracking: Aggregating Crowdsourced Structured Time Series Data (NW, DYY), pp. 1107–1115.

ICPR-2014-BagheriGE #approach #classification #novel #subclass

Generic Subclass Ensemble: A Novel Approach to Ensemble Classification (MAB, QG, SE), pp. 1254–1259.

ICPR-2014-BashbaghiGSB #multi #using

Watch-List Screening Using Ensembles Based on Multiple Face Representations (SB, EG, RS, GAB), pp. 4489–4494.

ICPR-2014-ChakeriH #approach #clustering #framework #game studies #set

Dominant Sets as a Framework for Cluster Ensembles: An Evolutionary Game Theory Approach (AC, LOH), pp. 3457–3462.

ICPR-2014-CruzSC #on the

On Meta-learning for Dynamic Ensemble Selection (RMOC, RS, GDCC), pp. 1230–1235.

ICPR-2014-DumonceauxRG #algebra #approach #clustering

An Algebraic Approach to Ensemble Clustering (FD, GR, MG), pp. 1301–1306.

ICPR-2014-KrawczykWC #classification #clustering #fuzzy

Weighted One-Class Classifier Ensemble Based on Fuzzy Feature Space Partitioning (BK, MW, BC), pp. 2838–2843.

ICPR-2014-LiYLYWH #classification #multi #predict

Multi-view Based AdaBoost Classifier Ensemble for Class Prediction from Gene Expression Profiles (LL, ZY, JL, JY, HSW, GH), pp. 178–183.

ICPR-2014-TaoIWS #approximate #data transformation #rank #representation

Ensemble Manifold Structured Low Rank Approximation for Data Representation (LT, HHSI, YW, XS), pp. 744–749.

ICPR-2014-YangYH #learning

Diversity-Based Ensemble with Sample Weight Learning (CY, XCY, HWH), pp. 1236–1241.

MLDM-2014-AlshdaifatCD #classification #multi

A Multi-path Strategy for Hierarchical Ensemble Classification (EA, FC, KD), pp. 198–212.

MLDM-2014-LimsettoW

Integrating Weight with Ensemble to Handle Changes in Class Distribution (NL, KW), pp. 91–106.

RecSys-2014-TangJLL #personalisation #recommendation

Ensemble contextual bandits for personalized recommendation (LT, YJ, LL, TL), pp. 73–80.

SEKE-2014-GaoKN #estimation #learning #quality #ranking

Comparing Two Approaches for Adding Feature Ranking to Sampled Ensemble Learning for Software Quality Estimation (KG, TMK, AN), pp. 280–285.

SIGIR-2014-LiuL #learning #probability #segmentation #word

Probabilistic ensemble learning for vietnamese word segmentation (WL, LL), pp. 931–934.

SIGIR-2014-TangJY #optimisation #ranking #runtime

Cache-conscious runtime optimization for ranking ensembles (XT, XJ, TY), pp. 1123–1126.

SAC-2014-GomesE #adaptation #classification #data type #named #social

SAE2: advances on the social adaptive ensemble classifier for data streams (HMG, FE), pp. 798–804.

HPDC-2014-WangZCLR #generative

Next generation job management systems for extreme-scale ensemble computing (KW, XZ, HC, ML, IR), pp. 111–114.

CBSE-2013-BarnatBCP #component #named #verification

DCCL: verification of component systems with ensembles (JB, NB, IC, ZP), pp. 43–52.

CBSE-2013-BuresGHKKP #component #named

DEECO: an ensemble-based component system (TB, IG, PH, JK, MK, FP), pp. 81–90.

CBSE-2013-KezniklBPGHH #component #design #invariant #refinement

Design of ensemble-based component systems by invariant refinement (JK, TB, FP, IG, PH, NH), pp. 91–100.

DATE-2013-PaoneVZSMHL #embedded #manycore #modelling #simulation

Improving simulation speed and accuracy for many-core embedded platforms with ensemble models (EP, NV, VZ, CS, DM, GH, TL), pp. 671–676.

ICDAR-2013-MoghaddamMC #automation #documentation #framework #image

Unsupervised Ensemble of Experts (EoE) Framework for Automatic Binarization of Document Images (RFM, FFM, MC), pp. 703–707.

ICDAR-2013-SuL #learning #recognition

Discriminative Weighting and Subspace Learning for Ensemble Symbol Recognition (FS, TL), pp. 1088–1092.

ICDAR-2013-YanYWYYH #classification #sorting

Sorting-Based Dynamic Classifier Ensemble Selection (YY, XCY, ZBW, XY, CY, HWH), pp. 673–677.

HCI-AS-2013-TakanoS #learning

Nature Sound Ensemble Learning in Narrative-Episode Creation with Pictures (KT, SS), pp. 493–502.

HCI-IMT-2013-SakamotoTT #music #using

A Method for Discussing Musical Expression between Music Ensemble Players Using a Web-Based System (TS, ST, JT), pp. 730–739.

KDD-2013-ZimekGCS #detection #effectiveness #performance

Subsampling for efficient and effective unsupervised outlier detection ensembles (AZ, MG, RJGBC, JS), pp. 428–436.

SEKE-2013-GaoKN #estimation #preprocessor #quality

Exploring Ensemble-Based Data Preprocessing Techniques for Software Quality Estimation (KG, TMK, AN), pp. 612–617.

PPDP-2013-LamC #constraints #distributed #execution

Decentralized execution of constraint handling rules for ensembles (ESLL, IC), pp. 205–216.

WICSA-ECSA-2012-KezniklBPK #component #towards

Towards Dependable Emergent Ensembles of Components: The DEECo Component Model (JK, TB, FP, MK), pp. 249–252.

CASE-2012-MatherH #automation #modelling

Ensemble modeling and control for congestion management in automated warehouses (TWM, MAH), pp. 390–395.

DRR-2012-Obafemi-AjayiAX #classification #documentation

Ensemble methods with simple features for document zone classification (TOA, GA, BX).

CHI-2012-HeinrichsSHM #formal method #interactive #mobile #towards

Toward a theory of interaction in mobile paper-digital ensembles (FH, DS, JH, MM), pp. 1897–1900.

CIKM-2012-EldardiryN #analysis #classification #graph #how #predict

An analysis of how ensembles of collective classifiers improve predictions in graphs (HE, JN), pp. 225–234.

CIKM-2012-GuoMCJ #learning #recommendation #social

Learning to recommend with social relation ensemble (LG, JM, ZC, HJ), pp. 2599–2602.

CIKM-2012-LiBCH #clustering #learning #relational

Relational co-clustering via manifold ensemble learning (PL, JB, CC, ZH), pp. 1687–1691.

ECIR-2012-ToramanC #categorisation #performance

Squeezing the Ensemble Pruning: Faster and More Accurate Categorization for News Portals (CT, FC), pp. 508–511.

ICML-2012-HannahD #design #geometry #programming

Ensemble Methods for Convex Regression with Applications to Geometric Programming Based Circuit Design (LH, DBD), p. 24.

ICPR-2012-HiradeY #learning #predict

Ensemble learning for change-point prediction (RH, TY), pp. 1860–1863.

ICPR-2012-MaoYLZ #classification #invariant #verification

Age-invariant face verification based on Local Classifier Ensemble (XJM, YBY, NL, YZ), pp. 2408–2411.

ICPR-2012-RadtkeGSG #adaptation

Adaptive selection of ensembles for imbalanced class distributions (PVWR, EG, RS, DOG), pp. 2980–2984.

ICPR-2012-SuYL #recognition

Ensemble symbol recognition with Hough forest (FS, LY, TL), pp. 1659–1662.

ICPR-2012-TuS #adaptation #classification #learning

Dynamical ensemble learning with model-friendly classifiers for domain adaptation (WT, SS), pp. 1181–1184.

KDD-2012-Williams

Ensembles and model delivery for tax compliance (GW), p. 1003.

KDD-2012-YuDRZY #classification #multi #predict

Transductive multi-label ensemble classification for protein function prediction (GXY, CD, HR, GZ, ZY), pp. 1077–1085.

KDIR-2012-CheungZZ #approach #network

A Bayesian Approach for Constructing Ensemble Neural Network (SHC, YZ, ZZ), pp. 374–377.

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.

SIGIR-2012-LipkaSA #classification #clustering #information retrieval #problem

Cluster-based one-class ensemble for classification problems in information retrieval (NL, BS, MA), pp. 1041–1042.

SAT-2012-JarvisaloKKK #performance

Finding Efficient Circuits for Ensemble Computation (MJ, PK, MK, JHK), pp. 369–382.

ICDAR-2011-ManoharVCPN #clustering #graph #segmentation

Graph Clustering-Based Ensemble Method for Handwritten Text Line Segmentation (VM, SNPV, HC, RP, PN), pp. 574–578.

ICDAR-2011-VajdaJF #approach #learning

A Semi-supervised Ensemble Learning Approach for Character Labeling with Minimal Human Effort (SV, AJ, GAF), pp. 259–263.

SIGMOD-2011-GulloDT #clustering

Advancing data clustering via projective clustering ensembles (FG, CD, AT), pp. 733–744.

CIKM-2011-VinzamuriK #classification #convergence #design #using

Designing an ensemble classifier over subspace classifiers using iterative convergence routine (BV, KK), pp. 693–698.

ICML-2011-JawanpuriaNR #kernel #learning #performance #using

Efficient Rule Ensemble Learning using Hierarchical Kernels (PJ, JSN, GR), pp. 161–168.

KDD-2011-ZhangLWGZG #data type #modelling #performance #predict

Enabling fast prediction for ensemble models on data streams (PZ, JL, PW, BJG, XZ, LG), pp. 177–185.

SEKE-2011-Collazo-MojicaS #distributed #metamodelling

A Metamodel for Distributed Ensembles of Virtual Appliances (XJCM, SMS), pp. 560–565.

SIGIR-2011-SandenZ #classification #multi #music

Enhancing multi-label music genre classification through ensemble techniques (CS, JZZ), pp. 705–714.

SAC-2011-GomesRS #concept #data type #learning

Learning recurring concepts from data streams with a context-aware ensemble (JBG, EMR, PACS), pp. 994–999.

CASE-2010-ParkSR #automation #classification #database

Image-based automated chemical database annotation with ensemble of machine-vision classifiers (JP, KS, GRR), pp. 168–173.

CAiSE-2010-DornD #adaptation #self

Interaction-Driven Self-adaptation of Service Ensembles (CD, SD), pp. 393–408.

ICPR-2010-AbdalaWJ #clustering #random

Ensemble Clustering via Random Walker Consensus Strategy (DDA, PW, XJ), pp. 1433–1436.

ICPR-2010-ArmanoH #prototype #random

Random Prototype-based Oracle for Selection-fusion Ensembles (GA, NH), pp. 77–80.

ICPR-2010-BayramDSM #approach #classification

An Ensemble of Classifiers Approach to Steganalysis (SB, AED, HTS, NDM), pp. 4376–4379.

ICPR-2010-Carneiro #automation #case study #comparative #design #image

A Comparative Study on the Use of an Ensemble of Feature Extractors for the Automatic Design of Local Image Descriptors (GC), pp. 3356–3359.

ICPR-2010-ErdoganS #classification #framework #learning #linear

A Unifying Framework for Learning the Linear Combiners for Classifier Ensembles (HE, MUS), pp. 2985–2988.

ICPR-2010-GuoBC #approach #learning #using

Support Vectors Selection for Supervised Learning Using an Ensemble Approach (LG, SB, NC), pp. 37–40.

ICPR-2010-KappSM #adaptation #incremental #learning

Adaptive Incremental Learning with an Ensemble of Support Vector Machines (MNK, RS, PM), pp. 4048–4051.

ICPR-2010-KimKP #algorithm #generative #simulation

A Simulation Study on the Generative Neural Ensemble Decoding Algorithms (SPK, MKK, GTP), pp. 3797–3800.

ICPR-2010-KotropoulosAP #classification #music

Ensemble Discriminant Sparse Projections Applied to Music Genre Classification (CK, GRA, YP), pp. 822–825.

ICPR-2010-LeeJJ #image #ranking #retrieval #scalability

Unsupervised Ensemble Ranking: Application to Large-Scale Image Retrieval (JEL, RJ, AKJ), pp. 3902–3906.

ICPR-2010-Mirzaei #algorithm #clustering #multi #novel

A Novel Multi-view Agglomerative Clustering Algorithm Based on Ensemble of Partitions on Different Views (HM), pp. 1007–1010.

ICPR-2010-PlumptonKLJ #classification #linear #online #using

On-Line fMRI Data Classification Using Linear and Ensemble Classifiers (COP, LIK, DEJL, SJJ), pp. 4312–4315.

ICPR-2010-SakarKSG #clustering #feature model #predict

Prediction of Protein Sub-nuclear Location by Clustering mRMR Ensemble Feature Selection (COS, OK, HS, FG), pp. 2572–2575.

ICPR-2010-SenkoK #clustering #pattern matching #pattern recognition #recognition #using

Pattern Recognition Method Using Ensembles of Regularities Found by Optimal Partitioning (OVS, AVK), pp. 2957–2960.

ICPR-2010-SmithW #analysis

A Bias-Variance Analysis of Bootstrapped Class-Separability Weighting for Error-Correcting Output Code Ensembles (RSS, TW), pp. 61–64.

ICPR-2010-WoloszynskiK #classification #random

A Measure of Competence Based on Randomized Reference Classifier for Dynamic Ensemble Selection (TW, MK), pp. 4194–4197.

ICPR-2010-WuLW #image #learning #retrieval #using

Enhancing SVM Active Learning for Image Retrieval Using Semi-supervised Bias-Ensemble (JW, ML, CLW), pp. 3175–3178.

ICPR-2010-ZhangW #clustering #named

ARImp: A Generalized Adjusted Rand Index for Cluster Ensembles (SZ, HSW), pp. 778–781.

KDD-2010-LuWZB

Ensemble pruning via individual contribution ordering (ZL, XW, XZ, JB), pp. 871–880.

KDD-2010-PrengerLVCH #bound #classification #fault

Class-specific error bounds for ensemble classifiers (RJP, TDL, KRV, BYC, WGH), pp. 843–852.

KDD-2010-YeLCJ #automation #categorisation #clustering #using

Automatic malware categorization using cluster ensemble (YY, TL, YC, QJ), pp. 95–104.

SEKE-2010-WangKG #classification #feature model #quality

Ensemble Feature Selection Technique for Software Quality Classification (HW, TMK, KG), pp. 215–220.

SAC-2010-FernandesLR #classification #random

The impact of random samples in ensemble classifiers (PF, LL, DDAR), pp. 1002–1009.

VLDB-2009-PandaHBB #learning #named #parallel #pipes and filters

PLANET: Massively Parallel Learning of Tree Ensembles with MapReduce (BP, JH, SB, RJB), pp. 1426–1437.

CHI-2009-TalbotLKT #classification #interactive #machine learning #multi #named #visualisation

EnsembleMatrix: interactive visualization to support machine learning with multiple classifiers (JT, BL, AK, DST), pp. 1283–1292.

HCI-NIMT-2009-JohnsonK #classification

Ensemble SWLDA Classifiers for the P300 Speller (GDJ, DJK), pp. 551–557.

ICEIS-J-2009-SchclarR #classification #random

Random Projection Ensemble Classifiers (AS, LR), pp. 309–316.

CIKM-2009-ChinavleKOF #classification

Ensembles in adversarial classification for spam (DC, PK, TO, TF), pp. 2015–2018.

CIKM-2009-WuZH #clustering

Fragment-based clustering ensembles (OW, MZ, WH), pp. 1795–1798.

KDD-2009-BifetHPKG #data type #evolution

New ensemble methods for evolving data streams (AB, GH, BP, RK, RG), pp. 139–148.

KDIR-2009-DuarteDRF #clustering #consistency #using

Cluster Ensemble Selection — Using Average Cluster Consistency (FJFD, JMMD, FR, ALNF), pp. 85–95.

MLDM-2009-DuangsoithongW #analysis #classification

Relevance and Redundancy Analysis for Ensemble Classifiers (RD, TW), pp. 206–220.

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-Mendes-MoreiraJSS #approach #case study #learning

Ensemble Learning: A Study on Different Variants of the Dynamic Selection Approach (JMM, AMJ, CS, JFdS), pp. 191–205.

MLDM-2009-RosenthalVHHL

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

RecSys-2009-SchclarTRMA #collaboration #performance

Ensemble methods for improving the performance of neighborhood-based collaborative filtering (AS, AT, LR, AM, LA), pp. 261–264.

SIGIR-2009-MaKL #learning #recommendation #social #trust

Learning to recommend with social trust ensemble (HM, IK, MRL), pp. 203–210.

SAC-2009-FodehPT #clustering #documentation #semantics #statistics

Combining statistics and semantics via ensemble model for document clustering (SJF, WFP, PNT), pp. 1446–1450.

ICLP-2009-Ashley-RollmanLGPC #independence #scalability

A Language for Large Ensembles of Independently Executing Nodes (MPAR, PL, SCG, PP, JC), pp. 265–280.

DRR-2008-Obafemi-AjayiAF #classification #documentation

Ensemble LUT classification for degraded document enhancement (TOA, GA, OF), p. 681509.

CIKM-2008-HuangMG #categorisation #framework #multi

Error-driven generalist+experts (edge): a multi-stage ensemble framework for text categorization (JH, OM, CLG), pp. 83–92.

ICML-2008-DembczynskiKS

Maximum likelihood rule ensembles (KD, WK, RS), pp. 224–231.

ICPR-2008-WuF #3d #classification #learning #multi #using

Multiple view based 3D object classification using ensemble learning of local subspaces (JW, KF), pp. 1–4.

ICPR-2008-YuW #3d #classification #clustering #knowledge base

Knowledge based cluster ensemble for 3D head model classification (ZY, HSW), pp. 1–4.

FSE-2008-KulturTB #estimation #memory management #named #network #using

ENNA: software effort estimation using ensemble of neural networks with associative memory (YK, BT, ABB), pp. 330–338.

ICDAR-2007-KoSB

K-Nearest Oracle for Dynamic Ensemble Selection (AHRK, RS, AdSBJ), pp. 422–426.

TACAS-2007-ElkindGP #detection #sequence chart

Detecting Races in Ensembles of Message Sequence Charts (EE, BG, DP), pp. 420–434.

ICEIS-AIDSS-2007-Cuzzocrea #multi

MRE-KDD+: A Multi-Resolution, Ensemble-Based Model for Advanced Knolwedge Discovery (AC), pp. 152–158.

CIKM-2007-LiuTZ #learning #network

Ensembling Bayesian network structure learning on limited data (FL, FT, QZ), pp. 927–930.

MLDM-2007-SilvaGF #identification

One Lead ECG Based Personal Identification with Feature Subspace Ensembles (HS, HG, ALNF), pp. 770–783.

RecSys-2007-TiemannP #hybrid #learning #music #recommendation #towards

Towards ensemble learning for hybrid music recommendation (MT, SP), pp. 177–178.

SIGIR-2007-SevillanoAS #clustering #named

BordaConsensus: a new consensus function for soft cluster ensembles (XS, FA, JCS), pp. 743–744.

CIKM-2006-HuZZ #array #clustering #identification #integration #mining

Integration of cluster ensemble and EM based text mining for microarray gene cluster identification and annotation (XH, XZ, XZ), pp. 824–825.

ICML-2006-DeCoste #collaboration #matrix #predict #using

Collaborative prediction using ensembles of Maximum Margin Matrix Factorizations (DD), pp. 249–256.

ICML-2006-Martinez-MunozS #order

Pruning in ordered bagging ensembles (GMM, AS), pp. 609–616.

ICPR-v1-2006-LiCF #hybrid #kernel #set

Hybrid Kernel Machine Ensemble for Imbalanced Data Sets (PL, KLC, WF), pp. 1108–1111.

ICPR-v2-2006-GaoLL #approach #classification #learning #optimisation

An ensemble classifier learning approach to ROC optimization (SG, CHL, JHL), pp. 679–682.

ICPR-v2-2006-ViswanathJ #clustering #performance

A Fast and Efficient Ensemble Clustering Method (PV, KJ), pp. 720–723.

ICPR-v3-2006-SmithKHI #recognition #using

Face Recognition Using Angular LDA and SVM Ensembles (RSS, JK, MH, JI), pp. 1008–1012.

ICPR-v4-2006-KoSB #random

A New Objective Function for Ensemble Selection in Random Subspaces (AHRK, RS, AdSBJ), pp. 185–188.

ICPR-v4-2006-LefaucheurN #classification #multi #robust #symmetry

Robust Multiclass Ensemble Classifiers via Symmetric Functions (PL, RN), pp. 136–139.

ICPR-v4-2006-ShanZSCG06a #recognition

Ensemble of Piecewise FDA Based on Spatial Histograms of Local (Gabor) Binary Patterns for Face Recognition (SS, WZ, YS, XC, WG), pp. 606–609.

SIGIR-2006-SevillanoCAS #clustering #documentation #robust

Feature diversity in cluster ensembles for robust document clustering (XS, GC, FA, JCS), pp. 697–698.

SAC-2006-BiD #approach

An evidential approach in ensembles (YB, WD), pp. 1–6.

ICSE-2006-TwalaCS #predict

Ensemble of missing data techniques to improve software prediction accuracy (BT, MC, MJS), pp. 909–912.

ICLP-2006-BaralDT #composition #metaprogramming #set

Macros, Macro Calls and Use of Ensembles in Modular Answer Set Programming (CB, JD, HT), pp. 376–390.

ICDAR-2005-BhattacharyaC #classification #recognition

Fusion of Combination Rules of an Ensemble of MLP Classifiers for Improved Recognition Accuracy of Handprinted Bangla Numerals (UB, BBC), pp. 322–326.

ICDAR-2005-RadtkeSW #classification #feature model

Intelligent Feature Extraction for Ensemble of Classifiers (PVWR, RS, TW), pp. 866–870.

ICML-2005-CarneyCDL #network #predict #probability #using

Predicting probability distributions for surf height using an ensemble of mixture density networks (MC, PC, JD, CL), pp. 113–120.

ICML-2005-EspositoS #classification #comparison #monte carlo

Experimental comparison between bagging and Monte Carlo ensemble classification (RE, LS), pp. 209–216.

ICML-2005-KhoussainovHK #classification

Ensembles of biased classifiers (RK, AH, NK), pp. 425–432.

ICML-2005-KolterM #concept #using

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

KDD-2005-GondekH #clustering

Non-redundant clustering with conditional ensembles (DG, TH), pp. 70–77.

KDD-2005-HeC #approach #robust

Making holistic schema matching robust: an ensemble approach (BH, KCCC), pp. 429–438.

MLDM-2005-KoK #classification #on the

On ECOC as Binary Ensemble Classifiers (JK, EK), pp. 1–10.

SAC-2005-DongH #classification #clustering #parametricity

Text classification based on data partitioning and parameter varying ensembles (YSD, KSH), pp. 1044–1048.

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

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

ICEIS-v2-2004-KotsiantisP #classification #hybrid #using

A Hybrid Decision Support Tool — Using Ensemble of Classifiers (SBK, PEP), pp. 448–456.

ICML-2004-CaruanaNCK #library #modelling

Ensemble selection from libraries of models (RC, ANM, GC, AK).

ICML-2004-EspositoS #analysis #classification #monte carlo

A Monte Carlo analysis of ensemble classification (RE, LS).

ICML-2004-FernB #clustering #graph #problem

Solving cluster ensemble problems by bipartite graph partitioning (XZF, CEB).

ICML-2004-FrankK #multi #problem

Ensembles of nested dichotomies for multi-class problems (EF, SK).

ICML-2004-MelvilleM #learning

Diverse ensembles for active learning (PM, RJM).

ICPR-v1-2004-DomeniconiY #nearest neighbour

Nearest Neighbor Ensemble (CD, BY), pp. 228–231.

ICPR-v1-2004-GunterB #evaluation #feature model #recognition #word

An Evaluation of Ensemble Methods in Handwritten Word Recognition Based on Feature Selection (SG, HB), pp. 388–392.

ICPR-v1-2004-TopchyMJP #adaptation #clustering

Adaptive Clustering Ensembles (APT, BMB, AKJ, WFP), pp. 272–275.

ICPR-v1-2004-ZouariHLA #classification #performance #simulation

Simulating Classifier Ensembles of Fixed Diversity for Studying Plurality Voting Performance (HZ, LH, YL, AMA), pp. 232–235.

ICPR-v3-2004-HoiL #feedback

Group-based Relevance Feedback with Support Vector Machine Ensembles (SCHH, MRL), pp. 874–877.

ICPR-v3-2004-LiCKG #classification #detection #image

Detecting Abnormal Regions in Colonoscopic Images by Patch-based Classifier Ensemble (PL, KLC, SMK, YG), pp. 774–777.

ICPR-v3-2004-LucasH #recognition #sequence

Sequence Recognition with Scanning N-Tuple Ensembles (SML, TKH), pp. 410–413.

ICPR-v3-2004-Windeatt #classification #design

Diversity/Accuracy and Ensemble Classifier Design (TW), pp. 454–457.

SEKE-2004-KhoshgoftaarJ #case study #classification #quality

Noise Elimination with Ensemble-Classifier Filtering: A Case-Study in Software Quality Engineerin (TMK, VHJ), pp. 226–231.

ICDAR-2003-OliveiraSBS #algorithm #approach #feature model #multi #search-based

Feature Selection for Ensembles: A Hierarchical Multi-Objective Genetic Algorithm Approach (LESdO, RS, FB, CYS), p. 676–?.

ICDAR-2003-TakahashiN #learning #recognition

A class-modular GLVQ ensemble with outlier learning for handwritten digit recognition (KT, DN), pp. 268–272.

ICML-2003-BrownW #ambiguity #composition #learning #network

The Use of the Ambiguity Decomposition in Neural Network Ensemble Learning Methods (GB, JLW), pp. 67–74.

ICML-2003-FernB03a #approach #clustering #random

Random Projection for High Dimensional Data Clustering: A Cluster Ensemble Approach (XZF, CEB), pp. 186–193.

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

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

VLDB-2002-SantiniG #analysis #geometry #named

GeMBASE: A Geometric Mediator for Brain Analysis with Surface Ensembles (SS, AG), pp. 1075–1078.

ICML-2002-GoebelRB #composition #performance #predict

A Unified Decomposition of Ensemble Loss for Predicting Ensemble Performance (MG, PJR, MB), pp. 211–218.

ICPR-v2-2002-GargPH #classification #network

Bayesian Networks as Ensemble of Classifiers (AG, VP, TSH), pp. 779–784.

ICPR-v2-2002-KimPJKB #classification #using

Pattern Classification Using Support Vector Machine Ensemble (HCK, SP, HMJ, DK, SYB), pp. 160–163.

ICPR-v2-2002-WindeattA

Tree Pruning for Output Coded Ensembles (TW, GA), pp. 92–95.

ICPR-v4-2002-LamXS #difference #using

Differentiation between Alphabetic and Numeric Data Using NN Ensembles (LL, QX, CYS), pp. 40–43.

KDD-2002-BennettDM

Exploiting unlabeled data in ensemble methods (KPB, AD, RM), pp. 289–296.

KDD-2002-KolczSK #classification #performance #random

Efficient handling of high-dimensional feature spaces by randomized classifier ensembles (AK, XS, JKK), pp. 307–313.

CIKM-2001-GohCC #classification #image

SVM Binary Classifier Ensembles for Image Classification (KG, EYC, KTC), pp. 395–402.

KDD-2001-IndurkhyaW #classification #problem #rule-based

Solving regression problems with rule-based ensemble classifiers (NI, SMW), pp. 287–292.

KDD-2001-KeoghCP #approach #database #named #scalability

Ensemble-index: a new approach to indexing large databases (EJK, SC, MJP), pp. 117–125.

KDD-2001-StreetK #algorithm #classification #scalability #streaming

A streaming ensemble algorithm (SEA) for large-scale classification (WNS, YK), pp. 377–382.

MLDM-2001-IndurkhyaW #rule-based

Rule-Based Ensemble Solutions for Regression (NI, SMW), pp. 62–72.

LCTES-OM-2001-CadotKLRS #communication #embedded #multi #named

ENSEMBLE: A Communication Layer for Embedded Multi-Processor Systems (SC, FK, KL, KvR, HJS), pp. 56–63.

SAT-2001-NuallainRB #behaviour #predict #satisfiability

Ensemble-based prediction of SAT search behaviour (BÓN, MdR, JvB), pp. 278–289.

ICML-2000-EvgeniouPPP #bound #kernel #performance

Bounds on the Generalization Performance of Kernel Machine Ensembles (TE, LPB, MP, TP), pp. 271–278.

ICML-2000-FernG #empirical #learning #online

Online Ensemble Learning: An Empirical Study (AF, RG), pp. 279–286.

ICML-2000-Howe #comparison #representation

Data as Ensembles of Records: Representation and Comparison (NRH), pp. 391–398.

ICML-2000-PennockMGH #algorithm #learning

A Normative Examination of Ensemble Learning Algorithms (DMP, PMRI, CLG, EH), pp. 735–742.

SAC-2000-AlhamedL #analysis #approach #clustering #multi #using

A Clustering Approach to Multi-Model Ensemble Analysis Using SAMEX Data (AA, SL), pp. 111–116.

TACAS-1999-HickeyLR #proving #specification

Specifications and Proofs for Ensemble Layers (JH, NAL, RvR), pp. 119–133.

ICPR-1996-GuttaHTW #network #recognition #using

Face recognition using ensembles of networks (SG, JH, BT, HW), pp. 50–54.

CSCW-1992-Newman-WolfeWM #concurrent #editing #object-oriented

Implicit Locking in the Ensemble Concurrent Object-Oriented Graphics Editor (RENW, MLW, MM), pp. 265–272.

STOC-1982-NivatP

Ensembles Reconnaissables de Mots Biinfinis (MN, DP), pp. 47–59.