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Stem boost$ (all stems)

276 papers:

DATEDATE-2015-MotamanGR #adaptation #robust
Impact of process-variations in STTRAM and adaptive boosting for robustness (SM, SG, NR), pp. 1431–1436.
DRRDRR-2015-PengCN #approach #using
Boost OCR accuracy using iVector based system combination approach (XP, HC, PN).
VLDBVLDB-2015-AlvanakiGIKK #navigation
GIS Navigation Boosted by Column Stores (FA, RG, MI, MLK, KK), pp. 1956–1967.
ICMLICML-2015-BeygelzimerKL #adaptation #algorithm #online
Optimal and Adaptive Algorithms for Online Boosting (AB, SK, HL), pp. 2323–2331.
ICMLICML-2015-LeeY #category theory #predict #strict
Boosted Categorical Restricted Boltzmann Machine for Computational Prediction of Splice Junctions (TL, SY), pp. 2483–2492.
ICMLICML-2015-NockPF
Rademacher Observations, Private Data, and Boosting (RN, GP, AF), pp. 948–956.
KDDKDD-2015-CuiCHC #random
Optimal Action Extraction for Random Forests and Boosted Trees (ZC, WC, YH, YC), pp. 179–188.
SIGIRSIGIR-2015-SallesGRR #automation #classification #effectiveness #fault #named #random
BROOF: Exploiting Out-of-Bag Errors, Boosting and Random Forests for Effective Automated Classification (TS, MAG, VR, LCdR), pp. 353–362.
POPLPOPL-2015-CochranDLMV #synthesis
Program Boosting: Program Synthesis via Crowd-Sourcing (RAC, LD, BL, DM, MV), pp. 677–688.
SACSAC-2015-SilvaLP #locality #multi #replication
Boosting locality in multi-version partial data replication (JAS, JL, HP), pp. 1309–1314.
HPCAHPCA-2015-HsuZLMWMTD #named #query
Adrenaline: Pinpointing and reining in tail queries with quick voltage boosting (CHH, YZ, MAL, DM, TFW, JM, LT, RGD), pp. 271–282.
CAVCAV-2015-BeyerW #invariant
Boosting k-Induction with Continuously-Refined Invariants (DB, MD, PW), pp. 622–640.
DATEDATE-2014-ShenCQ #mobile #probability
Battery aware stochastic QoS boosting in mobile computing devices (HS, QC, QQ), pp. 1–4.
DRRDRR-2014-RicquebourgRPLC
Boosting bonsai trees for handwritten/printed text discrimination (YR, CR, BP, AL, BC), pp. 902105–12.
ICSMEICSME-2014-WongXZHZM #analysis #fault #locality #segmentation
Boosting Bug-Report-Oriented Fault Localization with Segmentation and Stack-Trace Analysis (CPW, YX, HZ, DH, LZ, HM), pp. 181–190.
CHICHI-2014-ORourkeHBDP #education #game studies #persistent
Brain points: a growth mindset incentive structure boosts persistence in an educational game (EO, KH, CB, CSD, ZP), pp. 3339–3348.
ICMLICML-c1-2014-ChenLL #multi #online #problem
Boosting with Online Binary Learners for the Multiclass Bandit Problem (STC, HTL, CJL), pp. 342–350.
ICMLICML-c1-2014-TaiebH #multi
Boosting multi-step autoregressive forecasts (SBT, RJH), pp. 109–117.
ICMLICML-c2-2014-BeijbomSKV #multi
Guess-Averse Loss Functions For Cost-Sensitive Multiclass Boosting (OB, MJS, DJK, NV), pp. 586–594.
ICMLICML-c2-2014-CortesMS
Deep Boosting (CC, MM, US), pp. 1179–1187.
ICMLICML-c2-2014-LefakisF #programming
Dynamic Programming Boosting for Discriminative Macro-Action Discovery (LL, FF), pp. 1548–1556.
ICMLICML-c2-2014-SunZZ #analysis #convergence
A Convergence Rate Analysis for LogitBoost, MART and Their Variant (PS, TZ, JZ), pp. 1251–1259.
ICPRICPR-2014-AliNB #classification #constraints #image #probability #scalability
Boosting Stochastic Newton with Entropy Constraint for Large-Scale Image Classification (WBHA, RN, MB), pp. 232–237.
ICPRICPR-2014-BuiNLYK #detection #order #statistics #using
Boosted Stable Path for Staff-Line Detection Using Order Statistic Downscaling and Coarse-to-Fine Technique (HNB, ISN, GL, HJY, SHK), pp. 522–526.
ICPRICPR-2014-GarciaMFGM #identification
Person Orientation and Feature Distances Boost Re-identification (JG, NM, GLF, AG, CM), pp. 4618–4623.
ICPRICPR-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.
ICPRICPR-2014-QianZX #classification #multi #social
Boosted Multi-modal Supervised Latent Dirichlet Allocation for Social Event Classification (SQ, TZ, CX), pp. 1999–2004.
KDDKDD-2014-XuHWZ #feature model
Gradient boosted feature selection (ZEX, GH, KQW, AXZ), pp. 522–531.
KDDKDD-2014-ZhaiXW #multi #optimisation
A multi-class boosting method with direct optimization (SZ, TX, SW), pp. 273–282.
RecSysRecSys-2014-ChengXZKL
Gradient boosting factorization machines (CC, FX, TZ, IK, MRL), pp. 265–272.
SIGIRSIGIR-2014-ZhangZ0LM #bibliography #classification #sentiment
Do users rate or review?: boost phrase-level sentiment labeling with review-level sentiment classification (YZ, HZ, MZ, YL, SM), pp. 1027–1030.
PPoPPPPoPP-2014-HassanPR #transaction
Optimistic transactional boosting (AH, RP, BR), pp. 387–388.
ICDARICDAR-2013-HuZ #multi #using
Segmenting Handwritten Math Symbols Using AdaBoost and Multi-scale Shape Context Features (LH, RZ), pp. 1180–1184.
ICDARICDAR-2013-ZhuZ #detection #image #recognition #using
Label Detection and Recognition for USPTO Images Using Convolutional K-Means Feature Quantization and Ada-Boost (SZ, RZ), pp. 633–637.
CIKMCIKM-2013-ZhouC #documentation
Entity-centric document filtering: boosting feature mapping through meta-features (MZ, KCCC), pp. 119–128.
ICMLICML-c1-2013-ChenLYY #functional #matrix #using
General Functional Matrix Factorization Using Gradient Boosting (TC, HL, QY, YY), pp. 436–444.
ICMLICML-c1-2013-PareekR
Human Boosting (HHP, PDR), pp. 338–346.
ICMLICML-c2-2013-Telgarsky
Margins, Shrinkage, and Boosting (MT), pp. 307–315.
ICMLICML-c3-2013-AppelFDP
Quickly Boosting Decision Trees — Pruning Underachieving Features Early (RA, TJF, PD, PP), pp. 594–602.
ICMLICML-c3-2013-SunZ #evaluation #order #representation
Saving Evaluation Time for the Decision Function in Boosting: Representation and Reordering Base Learner (PS, JZ), pp. 933–941.
KDDKDD-2013-SongLLY #classification #collaboration #microblog #process
Collaborative boosting for activity classification in microblogs (YS, ZL, CWkL, QY), pp. 482–490.
RecSysRecSys-2013-OstrikovRS #collaboration #metadata #using
Using geospatial metadata to boost collaborative filtering (AO, LR, BS), pp. 423–426.
SIGIRSIGIR-2013-AnH #analysis #information retrieval #probability #semantics
Boosting novelty for biomedical information retrieval through probabilistic latent semantic analysis (XA, JXH), pp. 829–832.
ESEC-FSEESEC-FSE-2013-JaffarMN #testing
Boosting concolic testing via interpolation (JJ, VM, JAN), pp. 48–58.
LCTESLCTES-2013-ChenY #comparison #detection #fault #performance
Boosting efficiency of fault detection and recovery throughapplication-specific comparison and checkpointing (HC, CY), pp. 13–20.
DATEDATE-2012-MengC #3d #analysis #energy #performance #runtime
Analysis and runtime management of 3D systems with stacked DRAM for boosting energy efficiency (JM, AKC), pp. 611–616.
VLDBVLDB-2012-NguyenHZW #clustering
Boosting Moving Object Indexing through Velocity Partitioning (TN, ZH, RZ, PW), pp. 860–871.
ICMLICML-2012-ChenLL #algorithm #online
An Online Boosting Algorithm with Theoretical Justifications (STC, HTL, CJL), p. 243.
ICMLICML-2012-SunRZ #adaptation #multi #named #problem
AOSO-LogitBoost: Adaptive One-Vs-One LogitBoost for Multi-Class Problem (PS, MDR, JZ), p. 103.
ICPRICPR-2012-AbouelenienY #analysis #fault #named #performance
SampleBoost: Improving boosting performance by destabilizing weak learners based on weighted error analysis (MA, XY), pp. 585–588.
ICPRICPR-2012-BougesCBL #classification #nearest neighbour #using
Using k-nearest neighbors to handle missing weak classifiers in a boosted cascade (PB, TC, CB, GL), pp. 1763–1766.
ICPRICPR-2012-ChangrampadiYG #classification #image #multi #visual notation
Multi-class ada-boost classification of object poses through visual and infrared image information fusion (MHC, YY, IYHG), pp. 2865–2868.
ICPRICPR-2012-GaoSH #online
Online Transfer Boosting for object tracking (CG, NS, RH), pp. 906–909.
ICPRICPR-2012-HajimirsadeghiM #multi
Multiple instance real boosting with aggregation functions (HH, GM), pp. 2706–2710.
ICPRICPR-2012-LiYLKZL #classification #multi #using
Multiclass boosting SVM using different texture features in HEp-2 cell staining pattern classification (KL, JY, ZL, XK, RZ, WL), pp. 170–173.
ICPRICPR-2012-LuJ #recognition
Audio-visual emotion recognition with boosted coupled HMM (KL, YJ), pp. 1148–1151.
ICPRICPR-2012-LuoKM #3d #using
Endoscope 3-D motion tracking using an aggressive particle filtering for boosting electromagnetic guidance endoscopy (XL, TK, KM), pp. 117–120.
ICPRICPR-2012-SharmaHN #classification #detection #incremental #learning #performance
Efficient incremental learning of boosted classifiers for object detection (PS, CH, RN), pp. 3248–3251.
ICPRICPR-2012-Utasi #classification #dataset
Weighted conditional mutual information based boosting for classification of imbalanced datasets (ÁU), pp. 2711–2714.
ICPRICPR-2012-YinYHI #effectiveness #geometry #image #locality
Effective text localization in natural scene images with MSER, geometry-based grouping and AdaBoost (XY, XCY, HWH, KI), pp. 725–728.
MLDMMLDM-2012-SyarifZPW #detection
Application of Bagging, Boosting and Stacking to Intrusion Detection (IS, EZ, APB, GW), pp. 593–602.
SIGIRSIGIR-2012-XiaWHJ #image #kernel #multi #retrieval #scalability
Boosting multi-kernel locality-sensitive hashing for scalable image retrieval (HX, PW, SCHH, RJ), pp. 55–64.
SACSAC-2012-Pizzuti #algorithm #community #composition #detection #search-based
Boosting the detection of modular community structure with genetic algorithms and local search (CP), pp. 226–231.
HPCAHPCA-2012-LinMHSC #named #performance
Parabix: Boosting the efficiency of text processing on commodity processors (DL, NM, KSH, AS, RDC), pp. 373–384.
DRRDRR-2011-FanSNMH #feature model #recognition
Natural scene logo recognition by joint boosting feature selection in salient regions (WF, JS, SN, AM, YH), pp. 1–10.
DRRDRR-2011-SuPD #recognition
SemiBoost-based Arabic character recognition method (BS, LP, XD), pp. 1–10.
DRRDRR-2011-XieA #classification
Boosting based text and non-text region classification (BX, GA), pp. 1–10.
ICDARICDAR-2011-LeeLLYK #detection
AdaBoost for Text Detection in Natural Scene (JJL, PHL, SWL, ALY, CK), pp. 429–434.
ITiCSEITiCSE-2011-PollockH #learning #multi
Combining multiple pedagogies to boost learning and enthusiasm (LLP, TH), pp. 258–262.
TACASTACAS-2011-CimattiNR #abstraction #lazy evaluation #partial order #reduction
Boosting Lazy Abstraction for SystemC with Partial Order Reduction (AC, IN, MR), pp. 341–356.
ECIRECIR-2011-CaiGWZ #adaptation #ranking
Weight-Based Boosting Model for Cross-Domain Relevance Ranking Adaptation (PC, WG, KFW, AZ), pp. 562–567.
ICMLICML-2011-GaoK #multi
Multiclass Boosting with Hinge Loss based on Output Coding (TG, DK), pp. 569–576.
ICMLICML-2011-GrubbB #algorithm #optimisation
Generalized Boosting Algorithms for Convex Optimization (AG, DB), pp. 1209–1216.
ICMLICML-2011-Reyzin #predict
Boosting on a Budget: Sampling for Feature-Efficient Prediction (LR), pp. 529–536.
MLDMMLDM-2011-ShengT #process
Boosting Inspired Process for Improving AUC (VSS, RT), pp. 199–209.
RecSysRecSys-2011-ForbesZ #matrix #recommendation
Content-boosted matrix factorization for recommender systems: experiments with recipe recommendation (PF, MZ), pp. 261–264.
RecSysRecSys-2011-KatzOSRS #collaboration #using #wiki
Using Wikipedia to boost collaborative filtering techniques (GK, NO, BS, LR, GS), pp. 285–288.
SIGIRSIGIR-2011-GanjisaffarCL #modelling #precise #ranking
Bagging gradient-boosted trees for high precision, low variance ranking models (YG, RC, CVL), pp. 85–94.
SIGIRSIGIR-2011-LvZC #approach #feedback #pseudo
A boosting approach to improving pseudo-relevance feedback (YL, CZ, WC), pp. 165–174.
ESEC-FSEESEC-FSE-2011-LiCK #analysis #performance #points-to #using
Boosting the performance of flow-sensitive points-to analysis using value flow (LL, CC, NK), pp. 343–353.
ECSAECSA-2010-ElorantaK #architecture #evaluation #using
Using Domain Knowledge to Boost Software Architecture Evaluation (VPE, KK), pp. 319–326.
SIGMODSIGMOD-2010-EmrichKKRZ
Boosting spatial pruning: on optimal pruning of MBRs (TE, HPK, PK, MR, AZ), pp. 39–50.
VLDBVLDB-2010-HayRMS #consistency
Boosting the Accuracy of Differentially Private Histograms Through Consistency (MH, VR, GM, DS), pp. 1021–1032.
CIKMCIKM-2010-DingSBVWLC #automation #detection #embedded #feature model #framework #image #using
Automatic detection of craters in planetary images: an embedded framework using feature selection and boosting (WD, TFS, LPCB, RV, YW, ZL, TC), pp. 749–758.
CIKMCIKM-2010-PavlovGB #hybrid #named #scalability
BagBoo: a scalable hybrid bagging-the-boosting model (DYP, AG, CAB), pp. 1897–1900.
CIKMCIKM-2010-TianHZLL #network #social
Boosting social network connectivity with link revival (YT, QH, QZ, XL, WCL), pp. 589–598.
ICMLICML-2010-Busa-FeketeK #performance #using
Fast boosting using adversarial bandits (RBF, BK), pp. 143–150.
ICMLICML-2010-GoldbergE #classification
Boosting Classifiers with Tightened L0-Relaxation Penalties (NG, JE), pp. 383–390.
ICMLICML-2010-GrubbB #composition #learning #network
Boosted Backpropagation Learning for Training Deep Modular Networks (AG, JAB), pp. 407–414.
ICMLICML-2010-PardoeS
Boosting for Regression Transfer (DP, PS), pp. 863–870.
ICPRICPR-2010-BattiatoFGMR
Boosting Gray Codes for Red Eyes Removal (SB, GMF, MG, GM, DR), pp. 4214–4217.
ICPRICPR-2010-FahnC #algorithm #realtime #recognition
Real-Time Upper-Limbs Posture Recognition Based on Particle Filters and AdaBoost Algorithms (CSF, SLC), pp. 3854–3857.
ICPRICPR-2010-HongCCG #detection #set
Boosted Sigma Set for Pedestrian Detection (XH, HC, XC, WG), pp. 3017–3020.
ICPRICPR-2010-HuoF #online #recognition #video
Online Boosting OC for Face Recognition in Continuous Video Stream (HH, JF), pp. 1233–1236.
ICPRICPR-2010-JhuoL #kernel #learning #multi #recognition
Boosted Multiple Kernel Learning for Scene Category Recognition (IHJ, DTL), pp. 3504–3507.
ICPRICPR-2010-JinHL #multi
Multi-class AdaBoost with Hypothesis Margin (XJ, XH, CLL), pp. 65–68.
ICPRICPR-2010-LechervyGP #interactive #retrieval
Active Boosting for Interactive Object Retrieval (AL, PHG, FP), pp. 3268–3271.
ICPRICPR-2010-LefakisWGS #detection
Boosted Edge Orientation Histograms for Grasping Point Detection (LL, HW, MPGT, LS), pp. 4072–4076.
ICPRICPR-2010-PiroNNB #classification
Boosting Bayesian MAP Classification (PP, RN, FN, MB), pp. 661–665.
ICPRICPR-2010-RenHHLC #detection
A Sample Pre-mapping Method Enhancing Boosting for Object Detection (HR, XH, CKH, LL, XC), pp. 3005–3008.
ICPRICPR-2010-SilveiraM #image #using
Boosting Alzheimer Disease Diagnosis Using PET Images (MS, JSM), pp. 2556–2559.
ICPRICPR-2010-SzczotFLP #adaptation #classification
Package Boosting for Readaption of Cascaded Classifiers (MS, JF, OL, GP), pp. 552–555.
ICPRICPR-2010-TakalaCP #clustering #network #sequence
Boosting Clusters of Samples for Sequence Matching in Camera Networks (VT, YC, MP), pp. 400–403.
ICPRICPR-2010-TorrentPLFSMP #approach #detection #using
Detecting Faint Compact Sources Using Local Features and a Boosting Approach (AT, MP, XL, JF, JRSS, JM, JMP), pp. 4613–4616.
ICPRICPR-2010-WangAYL #bottom-up #estimation #learning #top-down #using
Combined Top-Down/Bottom-Up Human Articulated Pose Estimation Using AdaBoost Learning (SW, HA, TY, SL), pp. 3670–3673.
ICPRICPR-2010-WangHL #analysis #incremental
Boosting Incremental Semi-supervised Discriminant Analysis for Tracking (HW, XH, CLL), pp. 2748–2751.
ICPRICPR-2010-ZhangYWWXHY #graph #multi #recognition
Multi-class Graph Boosting with Subgraph Sharing for Object Recognition (BZ, GY, YW, WW, JX, GH, YY), pp. 1541–1544.
KDDKDD-2010-ChapelleSVWZT #learning #multi #ranking #web
Multi-task learning for boosting with application to web search ranking (OC, PKS, SV, KQW, YZ, BLT), pp. 1189–1198.
KDDKDD-2010-FeiH #classification #functional #graph
Boosting with structure information in the functional space: an application to graph classification (HF, JH), pp. 643–652.
KDIRKDIR-2010-BalcazarTZ
Filtering Association Rules with Negations on the Basis of Their Confidence Boost (JLB, CT, MEZ), pp. 263–268.
SEKESEKE-2010-NetoFRR #design #evaluation #interface #multimodal #named #reuse #usability
MMWA-ae: boosting knowledge from Multimodal Interface Design, Reuse and Usability Evaluation (ATN, RPdMF, RGR, SOR), pp. 355–360.
HPCAHPCA-2010-LeeCC #multi #named #performance
StimulusCache: Boosting performance of chip multiprocessors with excess cache (HL, SC, BRC), pp. 1–12.
DACDAC-2009-PanKOMC #process
Selective wordline voltage boosting for caches to manage yield under process variations (YP, JK, SO, GM, SWC), pp. 57–62.
ICDARICDAR-2009-HanifP #algorithm #detection #image #locality #using
Text Detection and Localization in Complex Scene Images using Constrained AdaBoost Algorithm (SMH, LP), pp. 1–5.
HCIHCI-NIMT-2009-FahnKW #realtime #recognition
Real-Time Face Tracking and Recognition Based on Particle Filtering and AdaBoosting Techniques (CSF, MJK, KYW), pp. 198–207.
CIKMCIKM-2009-BatalH #classification #using
Boosting KNN text classification accuracy by using supervised term weighting schemes (IB, MH), pp. 2041–2044.
CIKMCIKM-2009-FernandezL #retrieval #using
Using opinion-based features to boost sentence retrieval (RTF, DEL), pp. 1617–1620.
CIKMCIKM-2009-PanCASD #feature model #ranking #using
Feature selection for ranking using boosted trees (FP, TC, DA, FS, GD), pp. 2025–2028.
CIKMCIKM-2009-ShenWCSYCL #classification
Exploiting term relationship to boost text classification (DS, JW, BC, JTS, QY, ZC, YL), pp. 1637–1640.
CIKMCIKM-2009-YeCCZ #distributed #probability
Stochastic gradient boosted distributed decision trees (JY, JHC, JC, ZZ), pp. 2061–2064.
CIKMCIKM-2009-ZhuCZWWC #algorithm #ranking
A general magnitude-preserving boosting algorithm for search ranking (CZ, WC, ZAZ, GW, DW, ZC), pp. 817–826.
ICMLICML-2009-DuchiS
Boosting with structural sparsity (JCD, YS), pp. 297–304.
ICMLICML-2009-Freund #game studies #learning #online
Invited talk: Drifting games, boosting and online learning (YF), p. 2.
ICMLICML-2009-KeglB #classification
Boosting products of base classifiers (BK, RBF), pp. 497–504.
ICMLICML-2009-Li #adaptation #classification #multi #named
ABC-boost: adaptive base class boost for multi-class classification (PL0), pp. 625–632.
ICMLICML-2009-WarmuthV #bibliography #optimisation #perspective #summary #tutorial
Tutorial summary: Survey of boosting from an optimization perspective (MKW, SVNV), p. 15.
KDDKDD-2009-ZhengWLL
Information theoretic regularization for semi-supervised boosting (LZ, SW, YL, CHL), pp. 1017–1026.
MLDMMLDM-2009-BarinovaV #named
ODDboost: Incorporating Posterior Estimates into AdaBoost (OB, DV), pp. 178–190.
MLDMMLDM-2009-MoedS #classification #performance
Efficient AdaBoost Region Classification (MM, ENS), pp. 123–136.
SACSAC-2009-ChenJZY #adaptation #performance
Boosting the performance of computing systems through adaptive configuration tuning (HC, GJ, HZ, KY), pp. 1045–1049.
SACSAC-2009-Xie #predict
Improved AdaBoost.M1 of decision trees with confidence-rated predictions (ZX), pp. 1462–1466.
PPoPPPPoPP-2009-KulkarniUCAV #how #transaction
Turbocharging boosted transactions or: how i learnt to stop worrying and love longer transactions (CEK, OSÜ, AC, EA, MV), pp. 307–308.
DRRDRR-2008-XiangTLW #recognition #reliability #using
Achieving high recognition reliability using decision trees and AdaBoost (JX, XT, YL, PSPW), p. 68150.
STOCSTOC-2008-KalaiMV #learning #on the
On agnostic boosting and parity learning (ATK, YM, EV), pp. 629–638.
CIKMCIKM-2008-BaoYFXSY #social #using
Boosting social annotations using propagation (SB, BY, BF, SX, ZS, YY), pp. 1507–1508.
CIKMCIKM-2008-NiXLH #approach #learning
Group-based learning: a boosting approach (WN, JX, HL, YH), pp. 1443–1444.
ICMLICML-2008-HaffariWWMJ
Boosting with incomplete information (GH, YW, SW, GM, FJ), pp. 368–375.
ICMLICML-2008-LoeffFR #approximate #learning #named
ManifoldBoost: stagewise function approximation for fully-, semi- and un-supervised learning (NL, DAF, DR), pp. 600–607.
ICPRICPR-2008-ChariotK #image #online
GPU-boosted online image matching (AC, RK), pp. 1–4.
ICPRICPR-2008-JiangD
Bhattacharyya boosting (YJ, XD), pp. 1–4.
ICPRICPR-2008-LuGZ #independence #interface
Subject-independent brain computer interface through boosting (SL, CG, HZ), pp. 1–4.
ICPRICPR-2008-SeiffertKHN #classification #named #performance
RUSBoost: Improving classification performance when training data is skewed (CS, TMK, JVH, AN), pp. 1–4.
ICPRICPR-2008-ShimosakaMS #process #recognition #robust
Robust indoor activity recognition via boosting (MS, TM, TS), pp. 1–4.
ICPRICPR-2008-SuAL #predict #realtime #robust
Robust real-time face alignment based on ASM with boosting regression for displacement prediction (YS, HA, SL), pp. 1–4.
ICPRICPR-2008-TsuchiyaF #feature model #using
A method of feature selection using contribution ratio based on boosting (MT, HF), pp. 1–4.
ICPRICPR-2008-VisentiniSF #detection #online
On-line boosted cascade for object detection (IV, LS, GLF), pp. 1–4.
ICPRICPR-2008-WangZ #collaboration #distributed #learning
Collaborative learning by boosting in distributed environments (SW, CZ), pp. 1–4.
ICPRICPR-2008-XieLZHW #image #recognition
Boosted cannabis image recognition (NX, XL, XZ, WH, JZW), pp. 1–4.
ICPRICPR-2008-YamashitaFLK #online
Human tracking based on Soft Decision Feature and online real boosting (TY, HF, SL, MK), pp. 1–4.
ICPRICPR-2008-ZaidiS #algorithm #detection
Confidence rated boosting algorithm for generic object detection (NAZ, DS), pp. 1–4.
ICPRICPR-2008-ZhangLHT #automation #classification
Boosting local feature descriptors for automatic objects classification in traffic scene surveillance (ZZ, ML, KH, TT), pp. 1–4.
KDDKDD-2008-LozanoA #multi
Multi-class cost-sensitive boosting with p-norm loss functions (ACL, NA), pp. 506–514.
RecSysRecSys-2008-DingZYZFB #collaboration #fault #predict #statistics
Boosting collaborative filtering based on statistical prediction errors (SD, SZ, QY, XZ, RF, LDB), pp. 3–10.
SIGIRSIGIR-2008-AminiTG #algorithm #learning #ranking
A boosting algorithm for learning bipartite ranking functions with partially labeled data (MRA, TVT, CG), pp. 99–106.
SACSAC-2008-BarczakJM #empirical #evaluation
Empirical evaluation of a new structure for AdaBoost (ALCB, MJJ, CHM), pp. 1764–1765.
SACSAC-2008-SuKZG #classification #collaboration #machine learning #using
Imputation-boosted collaborative filtering using machine learning classifiers (XS, TMK, XZ, RG), pp. 949–950.
PPoPPPPoPP-2008-HerlihyK #transaction
Transactional boosting: a methodology for highly-concurrent transactional objects (MH, EK), pp. 207–216.
DATEDATE-2007-CabodiNQ #induction #invariant #model checking
Boosting the role of inductive invariants in model checking (GC, SN, SQ), pp. 1319–1324.
DATEDATE-2007-DabrowskiR #interactive
Interactive presentation: Boosting SER test for RF transceivers by simple DSP technique (JD, RR), pp. 719–724.
DATEDATE-2007-FangH #hybrid #performance #satisfiability
A new hybrid solution to boost SAT solver performance (LF, MSH), pp. 1307–1313.
SIGMODSIGMOD-2007-MiloZV #clustering #topic
Boosting topic-based publish-subscribe systems with dynamic clustering (TM, TZ, EV), pp. 749–760.
ICMLICML-2007-DaiYXY #learning
Boosting for transfer learning (WD, QY, GRX, YY), pp. 193–200.
ICMLICML-2007-GeurtsWd #kernel
Gradient boosting for kernelized output spaces (PG, LW, FdB), pp. 289–296.
ICMLICML-2007-Masnadi-ShiraziV #symmetry
Asymmetric boosting (HMS, NV), pp. 609–619.
KDDKDD-2007-LiuJJ #clustering #constraints #named
BoostCluster: boosting clustering by pairwise constraints (YL, RJ, AKJ), pp. 450–459.
KDDKDD-2007-YanTS #classification #multi
Model-shared subspace boosting for multi-label classification (RY, JT, JRS), pp. 834–843.
SIGIRSIGIR-2007-BlancoB
Boosting static pruning of inverted files (RB, AB), pp. 777–778.
SIGIRSIGIR-2007-XuL #algorithm #information retrieval #named
AdaRank: a boosting algorithm for information retrieval (JX, HL), pp. 391–398.
SACSAC-2007-TanC #classification #using
Using hypothesis margin to boost centroid text classifier (ST, XC), pp. 398–403.
CIKMCIKM-2006-EguchiC #dependence #performance #query
Boosting relevance model performance with query term dependence (KE, WBC), pp. 792–793.
ICMLICML-2006-Li #clustering #multi
Multiclass boosting with repartitioning (LL), pp. 569–576.
ICMLICML-2006-ReyzinS #classification #complexity #how
How boosting the margin can also boost classifier complexity (LR, RES), pp. 753–760.
ICMLICML-2006-WarmuthLR #algorithm
Totally corrective boosting algorithms that maximize the margin (MKW, JL, GR), pp. 1001–1008.
ICPRICPR-v1-2006-ChengZS #detection
Boosted Gabor Features Applied to Vehicle Detection (HC, NZ, CS), pp. 662–666.
ICPRICPR-v1-2006-FuCLR #classification #feature model #image
Boosted Band Ratio Feature Selection for Hyperspectral Image Classification (ZF, TC, NL, ARK), pp. 1059–1062.
ICPRICPR-v1-2006-GaoW #random #recognition
Boosting in Random Subspaces for Face Recognition (YG, YW), pp. 519–522.
ICPRICPR-v1-2006-LinWZZ #optimisation
Continuous Optimization based-on Boosting Gaussian Mixture Model (BL, XW, RtZ, ZZ), pp. 1192–1195.
ICPRICPR-v1-2006-OgataCKI #detection #multi #process
Improving human activity detection by combining multi-dimensional motion descriptors with boosting (TO, WJC, JK, SI), pp. 295–298.
ICPRICPR-v1-2006-WangS06a #component #constraints #detection #geometry
Facial Components Detection with Boosting and Geometric Constraints (TW, PS), pp. 446–449.
ICPRICPR-v1-2006-ZhouW06a #using #verification
Face Verification Using GaborWavelets and AdaBoost (MZ, HW), pp. 404–407.
ICPRICPR-v2-2006-ChenSYCG #detection
Modification of the AdaBoost-based Detector for Partially Occluded Faces (JC, SS, SY, XC, WG), pp. 516–519.
ICPRICPR-v2-2006-DengHG #algorithm #classification
Ada-Boost Algorithm, Classification, Naïve- (WD, JH, JG), pp. 699–702.
ICPRICPR-v2-2006-LeS #detection #named #robust #using
Ent-Boost: Boosting Using Entropy Measure for Robust Object Detection (DDL, SS), pp. 602–605.
ICPRICPR-v2-2006-NiuSYCG #2d #locality
2D Cascaded AdaBoost for Eye Localization (ZN, SS, SY, XC, WG), pp. 1216–1219.
ICPRICPR-v2-2006-RamalhoM #detection #image #using
Using Boosting to Improve Oil Spill Detection in SAR Images (GLBR, FNSdM), pp. 1066–1069.
ICPRICPR-v2-2006-TerradesTV #adaptation #using
Combination of shape descriptors using an adaptation of boosting (ORT, ST, EV), pp. 764–767.
ICPRICPR-v2-2006-YuCMW #detection #markov #monte carlo #multi
Boosted Markov Chain Monte Carlo Data Association for Multiple Target Detection and Tracking (QY, IC, GGM, BW), pp. 675–678.
ICPRICPR-v3-2006-WangGZLZ #identification #using
Palmprint Identification using Boosting Local Binary Pattern (XW, HG, HZ, BL, ZZ), pp. 503–506.
ICPRICPR-v3-2006-XueD #2d #3d #locality #multi #using
3D+2D Face Localization Using Boosting in Multi-Modal Feature Space (FX, XD), pp. 499–502.
ICPRICPR-v4-2006-EscaleraPR #fault
Forest Extension of Error Correcting Output Codes and Boosted Landmarks (SE, OP, PR), pp. 104–107.
ICPRICPR-v4-2006-LeiDW #adaptation #embedded
AdaBoost Tracker Embedded in Adaptive Particle Filtering (YL, XD, SW), pp. 939–943.
ICDARICDAR-2005-BargeronVS #detection #learning
Boosting-based Transductive Learning for Text Detection (DB, PAV, PYS), pp. 1166–1171.
SIGMODSIGMOD-2005-ChenLL #on the #pattern matching #using #xml
On Boosting Holism in XML Twig Pattern Matching using Structural Indexing Techniques (TC, JL, TWL), pp. 455–466.
ICEISICEIS-v4-2005-DavulcuNR #semantic gap
Boosting Item Findability: Bridging the Semantic Gap between Search Phrases and Item Information (HD, HVN, VR), pp. 48–55.
CIKMCIKM-2005-VuG #retrieval #using
Using RankBoost to compare retrieval systems (HTV, PG), pp. 309–310.
ECIRECIR-2005-MishneR #query #retrieval #web
Boosting Web Retrieval Through Query Operations (GM, MdR), pp. 502–516.
ICMLICML-2005-JingPR #classification #learning #naive bayes #network #performance
Efficient discriminative learning of Bayesian network classifier via boosted augmented naive Bayes (YJ, VP, JMR), pp. 369–376.
ICMLICML-2005-JinZ #algorithm #probability #using
A smoothed boosting algorithm using probabilistic output codes (RJ, JZ), pp. 361–368.
ICMLICML-2005-SunTLW #framework
Unifying the error-correcting and output-code AdaBoost within the margin framework (YS, ST, JL, DW), pp. 872–879.
KDDKDD-2005-Momma #kernel #performance #scalability
Efficient computations via scalable sparse kernel partial least squares and boosted latent features (MM), pp. 654–659.
KDDKDD-2005-Rosset #robust
Robust boosting and its relation to bagging (SR), pp. 249–255.
MLDMMLDM-2005-SunWW #algorithm #parametricity
Parameter Inference of Cost-Sensitive Boosting Algorithms (YS, AKCW, YW), pp. 21–30.
SIGIRSIGIR-2005-HoweRM #documentation #recognition #retrieval #word
Boosted decision trees for word recognition in handwritten document retrieval (NRH, TMR, RM), pp. 377–383.
CCCC-2005-JiangMHLZZZ #multi #performance #using
Boosting the Performance of Multimedia Applications Using SIMD Instructions (WJ, CM, BH, JL, JZ, BZ, CZ), pp. 59–75.
DATEDATE-v2-2004-KahngMR #named
Boosting: Min-Cut Placement with Improved Signal Delay (ABK, ILM, SR), pp. 1098–1103.
STOCSTOC-2004-GuptaPRS #algorithm #approximate #optimisation #probability
Boosted sampling: approximation algorithms for stochastic optimization (AG, MP, RR, AS), pp. 417–426.
ICEISICEIS-v2-2004-DoP #data mining #mining #tool support #towards #visualisation
Towards High Dimensional Data Mining with Boosting of PSVM and Visualization Tools (TND, FP), pp. 36–41.
ICMLICML-2004-DietterichAB #random
Training conditional random fields via gradient tree boosting (TGD, AA, YB).
ICMLICML-2004-HertzBW #clustering #distance
Boosting margin based distance functions for clustering (TH, ABH, DW).
ICMLICML-2004-JanodetNSS #grammar inference
Boosting grammatical inference with confidence oracles (JCJ, RN, MS, HMS).
ICMLICML-2004-ZhangKY #algorithm
Surrogate maximization/minimization algorithms for AdaBoost and the logistic regression model (ZZ, JTK, DYY).
ICPRICPR-v1-2004-HeLZZ #classification #image #web
W-Boost and Its Application to Web Image Classification (JH, ML, HZ, CZ), pp. 148–151.
ICPRICPR-v1-2004-SochmanM
Inter-Stage Feature Propagation in Cascade Building with AdaBoost (JS, JM), pp. 236–239.
ICPRICPR-v1-2004-WuAL #detection
Glasses Detection by Boosting Simple Wavelet Features (BW, HA, RL), pp. 292–295.
ICPRICPR-v2-2004-HuangAWL #detection #multi
Boosting Nested Cascade Detector for Multi-View Face Detection (CH, HA, BW, SL), pp. 415–418.
ICPRICPR-v2-2004-ZhangLG #detection #realtime #using
Real-Time Face Detection Using Boosting in Hierarchical Feature Spaces (DZ, SZL, DGP), pp. 411–414.
ICPRICPR-v3-2004-HaugerW #multi
ECOC and Boosting with Multi-Layer Perceptrons (SH, TW), pp. 458–461.
ICPRICPR-v4-2004-WuTR04a
Resolution Enhancement by AdaBoost (JW, MMT, BDR), pp. 893–896.
KDDKDD-2004-BiZB #kernel
Column-generation boosting methods for mixture of kernels (JB, TZ, KPB), pp. 521–526.
DRRDRR-2003-LiuDF
AdaBoost-based handwritten/printed discrimination on a single character (HL, XD, CF), pp. 39–46.
DRRDRR-2003-RossetZH
Boosting and support vector machines as optimal separators (SR, JZ, TH), pp. 1–7.
SCAMSCAM-2003-BaggeKHV #c++ #design #optimisation #source code
Design of the CodeBoost Transformation System for Domain-Specific Optimisation of C++ Programs (OSB, KTK, MH, EV), p. 65–?.
STOCSTOC-2003-KalaiS
Boosting in the presence of noise (AK, RAS), pp. 195–205.
CIKMCIKM-2003-ShanahanR #classification
Boosting support vector machines for text classification through parameter-free threshold relaxation (JGS, NR), pp. 247–254.
CIKMCIKM-2003-ZhangOR #learning #using
Learning cross-document structural relationships using boosting (ZZ, JO, DRR), pp. 124–130.
ECIRECIR-2003-NardielloSS #categorisation
Discretizing Continuous Attributes in AdaBoost for Text Categorization (PN, FS, AS), pp. 320–334.
ICMLICML-2003-FernB #lazy evaluation
Boosting Lazy Decision Trees (XZF, CEB), pp. 178–185.
ICMLICML-2003-LeskovecS #dataset #linear #programming
Linear Programming Boosting for Uneven Datasets (JL, JST), pp. 456–463.
ICMLICML-2003-ZhangY #convergence #on the
On the Convergence of Boosting Procedures (TZ, BY), pp. 904–911.
SIGIRSIGIR-2003-CaiH #automation #categorisation #concept
Text categorization by boosting automatically extracted concepts (LC, TH), pp. 182–189.
CIKMCIKM-2002-LiuYC #bias #classification #induction
Boosting to correct inductive bias in text classification (YL, YY, JGC), pp. 348–355.
ICMLICML-2002-MeyerB #scalability #speech #towards
Towards “Large Margin” Speech Recognizers by Boosting and Discriminative Training (CM, PB), pp. 419–426.
ICMLICML-2002-ParkZ #learning
A Boosted Maximum Entropy Model for Learning Text Chunking (SBP, BTZ), pp. 482–489.
ICMLICML-2002-SchapireRRG #information management
Incorporating Prior Knowledge into Boosting (RES, MR, MGR, NKG), pp. 538–545.
ICMLICML-2002-SchapireSMLC #estimation #modelling #nondeterminism #using
Modeling Auction Price Uncertainty Using Boosting-based Conditional Density Estimation (RES, PS, DAM, MLL, JAC), pp. 546–553.
ICMLICML-2002-Zhang #behaviour #consistency #statistics
Statistical Behavior and Consistency of Support Vector Machines, Boosting, and Beyond (TZ0), pp. 690–700.
ICPRICPR-v2-2002-GrimPS #network #probability
Boosting in Probabilistic Neural Networks (JG, PP, PS), pp. 136–139.
ICPRICPR-v3-2002-ChoudhuryRPP #detection #learning #network
Boosting and Structure Learning in Dynamic Bayesian Networks for Audio-Visual Speaker Detection (TC, JMR, VP, AP), p. 789–?.
KDDKDD-2002-BennettME #algorithm #kernel #modelling #named
MARK: a boosting algorithm for heterogeneous kernel models (KPB, MM, MJE), pp. 24–31.
KDDKDD-2002-JoshiAK #predict #question
Predicting rare classes: can boosting make any weak learner strong? (MVJ, RCA, VK), pp. 297–306.
ICMLICML-2001-Codrington
Boosting with Confidence Information (CWC), pp. 59–65.
ICMLICML-2001-Das #feature model #hybrid
Filters, Wrappers and a Boosting-Based Hybrid for Feature Selection (SD), pp. 74–81.
ICMLICML-2001-Jiang #aspect-oriented #semistructured data
Some Theoretical Aspects of Boosting in the Presence of Noisy Data (WJ), pp. 234–241.
ICMLICML-2001-KriegerLW #semistructured data
Boosting Noisy Data (AK, CL, AJW), pp. 274–281.
ICMLICML-2001-SebbanNL #classification
Boosting Neighborhood-Based Classifiers (MS, RN, SL), pp. 505–512.
KDDKDD-2001-LazarevicO #algorithm #distributed
The distributed boosting algorithm (AL, ZO), pp. 311–316.
KDDKDD-2001-OzaR #online
Experimental comparisons of online and batch versions of bagging and boosting (NCO, SJR), pp. 359–364.
CIKMCIKM-2000-IyerLSSS #documentation
Boosting for Document Routing (RDI, DDL, RES, YS, AS), pp. 70–77.
CIKMCIKM-2000-SebastianiSV #algorithm #categorisation
An Improved Boosting Algorithm and its Application to Text Categorization (FS, AS, NV), pp. 78–85.
ICMLICML-2000-BennetDS #algorithm #generative
A Column Generation Algorithm For Boosting (KPB, AD, JST), pp. 65–72.
ICMLICML-2000-Mitchell
“Boosting” a Positive-Data-Only Learner (ARM), pp. 607–614.
ICMLICML-2000-MyersKSW #approach #topic
A Boosting Approach to Topic Spotting on Subdialogues (KM, MJK, SPS, MAW), pp. 655–662.
ICMLICML-2000-NomotoM #analysis #automation
Comparing the Minimum Description Length Principle and Boosting in the Automatic Analysis of Discourse (TN, YM), pp. 687–694.
ICMLICML-2000-OSullivanLCB #algorithm #named #robust
FeatureBoost: A Meta-Learning Algorithm that Improves Model Robustness (JO, JL, RC, AB), pp. 703–710.
ICMLICML-2000-Ting #algorithm #case study #comparative
A Comparative Study of Cost-Sensitive Boosting Algorithms (KMT), pp. 983–990.
ICPRICPR-v2-2000-PavlovMD #algorithm #using
Scaling-Up Support Vector Machines Using Boosting Algorithm (DP, JM, BD), pp. 2219–2222.
SIGIRSIGIR-2000-KimHZ #classification #naive bayes
Text filtering by boosting naive bayes classifiers (YHK, SYH, BTZ), pp. 168–175.
ICMLICML-1999-FanSZC #classification #named
AdaCost: Misclassification Cost-Sensitive Boosting (WF, SJS, JZ, PKC), pp. 97–105.
ICMLICML-1999-Harries
Boosting a Strong Learner: Evidence Against the Minimum Margin (MBH), pp. 171–180.
ICMLICML-1999-ZhengWT #lazy evaluation #learning #naive bayes
Lazy Bayesian Rules: A Lazy Semi-Naive Bayesian Learning Technique Competitive to Boosting Decision Trees (ZZ, GIW, KMT), pp. 493–502.
KDDKDD-1999-FanSZ #distributed #learning #online #scalability
The Application of AdaBoost for Distributed, Scalable and On-Line Learning (WF, SJS, JZ), pp. 362–366.
ICMLICML-1998-AbeM #learning #query #using
Query Learning Strategies Using Boosting and Bagging (NA, HM), pp. 1–9.
ICMLICML-1998-FreundISS #algorithm #performance
An Efficient Boosting Algorithm for Combining Preferences (YF, RDI, RES, YS), pp. 170–178.
KDDKDD-1998-RidgewayMRO #classification #naive bayes
Interpretable Boosted Naïve Bayes Classification (GR, DM, TR, JO), pp. 101–104.
SIGIRSIGIR-1998-SchapireSS
Boosting and Rocchio Applied to Text Filtering (RES, YS, AS), pp. 215–223.
ICMLICML-1997-Drucker #using
Improving Regressors using Boosting Techniques (HD), pp. 107–115.
ICMLICML-1997-MargineantuD #adaptation
Pruning Adaptive Boosting (DDM, TGD), pp. 211–218.
ICMLICML-1997-Schapire #learning #multi #problem #using
Using output codes to boost multiclass learning problems (RES), pp. 313–321.
ICMLICML-1997-SchapireFBL #effectiveness
Boosting the margin: A new explanation for the effectiveness of voting methods (RES, YF, PB, WSL), pp. 322–330.
HPDCHPDC-1997-FriedmanR #performance #protocol
Packing Messages as a Tool for Boosting the Performance of Total Ordering Protocols (RF, RvR), pp. 233–242.
STOCSTOC-1996-KearnsM #algorithm #learning #on the #top-down
On the Boosting Ability of Top-Down Decision Tree Learning Algorithms (MJK, YM), pp. 459–468.
ICMLICML-1996-FreundS #algorithm
Experiments with a New Boosting Algorithm (YF, RES), pp. 148–156.
ICLPJICSLP-1996-LhommeGRT #algorithm
Boosting the Interval Narrowing Algorithm (OL, AG, MR, PT), pp. 378–392.
ICMLICML-1994-DruckerCJCV #algorithm #machine learning
Boosting and Other Machine Learning Algorithms (HD, CC, LDJ, YL, VV), pp. 53–61.
ASPLOSASPLOS-1992-SmithHL #performance
Efficient Superscalar Performance Through Boosting (MDS, MH, MSL), pp. 248–259.
ICLPICLP-1987-Gallaire87 #logic programming
Boosting Logic Programming (HG), pp. 962–988.

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