Stem boost$ (all stems)

276 papers:

DATE-2015-MotamanGR #adaptation #robust

Impact of process-variations in STTRAM and adaptive boosting for robustness (SM, SG, NR), pp. 1431–1436.

DRR-2015-PengCN #approach #using

Boost OCR accuracy using iVector based system combination approach (XP, HC, PN).

VLDB-2015-AlvanakiGIKK #navigation

GIS Navigation Boosted by Column Stores (FA, RG, MI, MLK, KK), pp. 1956–1967.

ICML-2015-BeygelzimerKL #adaptation #algorithm #online

Optimal and Adaptive Algorithms for Online Boosting (AB, SK, HL), pp. 2323–2331.

ICML-2015-LeeY #category theory #predict #strict

Boosted Categorical Restricted Boltzmann Machine for Computational Prediction of Splice Junctions (TL, SY), pp. 2483–2492.

ICML-2015-NockPF

Rademacher Observations, Private Data, and Boosting (RN, GP, AF), pp. 948–956.

KDD-2015-CuiCHC #random

Optimal Action Extraction for Random Forests and Boosted Trees (ZC, WC, YH, YC), pp. 179–188.

SIGIR-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.

POPL-2015-CochranDLMV #synthesis

Program Boosting: Program Synthesis via Crowd-Sourcing (RAC, LD, BL, DM, MV), pp. 677–688.

SAC-2015-SilvaLP #locality #multi #replication

Boosting locality in multi-version partial data replication (JAS, JL, HP), pp. 1309–1314.

HPCA-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.

CAV-2015-BeyerW #invariant

Boosting k-Induction with Continuously-Refined Invariants (DB, MD, PW), pp. 622–640.

DATE-2014-ShenCQ #mobile #probability

Battery aware stochastic QoS boosting in mobile computing devices (HS, QC, QQ), pp. 1–4.

DRR-2014-RicquebourgRPLC

Boosting bonsai trees for handwritten/printed text discrimination (YR, CR, BP, AL, BC), pp. 902105–12.

ICSME-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.

CHI-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.

ICML-c1-2014-ChenLL #multi #online #problem

Boosting with Online Binary Learners for the Multiclass Bandit Problem (STC, HTL, CJL), pp. 342–350.

ICML-c1-2014-TaiebH #multi

Boosting multi-step autoregressive forecasts (SBT, RJH), pp. 109–117.

ICML-c2-2014-BeijbomSKV #multi

Guess-Averse Loss Functions For Cost-Sensitive Multiclass Boosting (OB, MJS, DJK, NV), pp. 586–594.

ICML-c2-2014-CortesMS

Deep Boosting (CC, MM, US), pp. 1179–1187.

ICML-c2-2014-LefakisF #programming

Dynamic Programming Boosting for Discriminative Macro-Action Discovery (LL, FF), pp. 1548–1556.

ICML-c2-2014-SunZZ #analysis #convergence

A Convergence Rate Analysis for LogitBoost, MART and Their Variant (PS, TZ, JZ), pp. 1251–1259.

ICPR-2014-AliNB #classification #constraints #image #probability #scalability

Boosting Stochastic Newton with Entropy Constraint for Large-Scale Image Classification (WBHA, RN, MB), pp. 232–237.

ICPR-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.

ICPR-2014-GarciaMFGM #identification

Person Orientation and Feature Distances Boost Re-identification (JG, NM, GLF, AG, CM), pp. 4618–4623.

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-QianZX #classification #multi #social

Boosted Multi-modal Supervised Latent Dirichlet Allocation for Social Event Classification (SQ, TZ, CX), pp. 1999–2004.

KDD-2014-XuHWZ #feature model

Gradient boosted feature selection (ZEX, GH, KQW, AXZ), pp. 522–531.

KDD-2014-ZhaiXW #multi #optimisation

A multi-class boosting method with direct optimization (SZ, TX, SW), pp. 273–282.

RecSys-2014-ChengXZKL

Gradient boosting factorization machines (CC, FX, TZ, IK, MRL), pp. 265–272.

SIGIR-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.

PPoPP-2014-HassanPR #transaction

Optimistic transactional boosting (AH, RP, BR), pp. 387–388.

ICDAR-2013-HuZ #multi #using

Segmenting Handwritten Math Symbols Using AdaBoost and Multi-scale Shape Context Features (LH, RZ), pp. 1180–1184.

ICDAR-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.

CIKM-2013-ZhouC #documentation

Entity-centric document filtering: boosting feature mapping through meta-features (MZ, KCCC), pp. 119–128.

ICML-c1-2013-ChenLYY #functional #matrix #using

General Functional Matrix Factorization Using Gradient Boosting (TC, HL, QY, YY), pp. 436–444.

ICML-c1-2013-PareekR

Human Boosting (HHP, PDR), pp. 338–346.

ICML-c2-2013-Telgarsky

Margins, Shrinkage, and Boosting (MT), pp. 307–315.

ICML-c3-2013-AppelFDP

Quickly Boosting Decision Trees — Pruning Underachieving Features Early (RA, TJF, PD, PP), pp. 594–602.

ICML-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.

KDD-2013-SongLLY #classification #collaboration #microblog #process

Collaborative boosting for activity classification in microblogs (YS, ZL, CWkL, QY), pp. 482–490.

RecSys-2013-OstrikovRS #collaboration #metadata #using

Using geospatial metadata to boost collaborative filtering (AO, LR, BS), pp. 423–426.

SIGIR-2013-AnH #analysis #information retrieval #probability #semantics

Boosting novelty for biomedical information retrieval through probabilistic latent semantic analysis (XA, JXH), pp. 829–832.

ESEC-FSE-2013-JaffarMN #testing

Boosting concolic testing via interpolation (JJ, VM, JAN), pp. 48–58.

LCTES-2013-ChenY #comparison #detection #fault #performance

Boosting efficiency of fault detection and recovery throughapplication-specific comparison and checkpointing (HC, CY), pp. 13–20.

DATE-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.

VLDB-2012-NguyenHZW #clustering

Boosting Moving Object Indexing through Velocity Partitioning (TN, ZH, RZ, PW), pp. 860–871.

ICML-2012-ChenLL #algorithm #online

An Online Boosting Algorithm with Theoretical Justifications (STC, HTL, CJL), p. 243.

ICML-2012-SunRZ #adaptation #multi #named #problem

AOSO-LogitBoost: Adaptive One-Vs-One LogitBoost for Multi-Class Problem (PS, MDR, JZ), p. 103.

ICPR-2012-AbouelenienY #analysis #fault #named #performance

SampleBoost: Improving boosting performance by destabilizing weak learners based on weighted error analysis (MA, XY), pp. 585–588.

ICPR-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.

ICPR-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.

ICPR-2012-GaoSH #online

Online Transfer Boosting for object tracking (CG, NS, RH), pp. 906–909.

ICPR-2012-HajimirsadeghiM #multi

Multiple instance real boosting with aggregation functions (HH, GM), pp. 2706–2710.

ICPR-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.

ICPR-2012-LuJ #recognition

Audio-visual emotion recognition with boosted coupled HMM (KL, YJ), pp. 1148–1151.

ICPR-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.

ICPR-2012-SharmaHN #classification #detection #incremental #learning #performance

Efficient incremental learning of boosted classifiers for object detection (PS, CH, RN), pp. 3248–3251.

ICPR-2012-Utasi #classification #dataset

Weighted conditional mutual information based boosting for classification of imbalanced datasets (ÁU), pp. 2711–2714.

ICPR-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.

MLDM-2012-SyarifZPW #detection

Application of Bagging, Boosting and Stacking to Intrusion Detection (IS, EZ, APB, GW), pp. 593–602.

SIGIR-2012-XiaWHJ #image #kernel #multi #retrieval #scalability

Boosting multi-kernel locality-sensitive hashing for scalable image retrieval (HX, PW, SCHH, RJ), pp. 55–64.

SAC-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.

HPCA-2012-LinMHSC #named #performance

Parabix: Boosting the efficiency of text processing on commodity processors (DL, NM, KSH, AS, RDC), pp. 373–384.

DRR-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.

DRR-2011-SuPD #recognition

SemiBoost-based Arabic character recognition method (BS, LP, XD), pp. 1–10.

DRR-2011-XieA #classification

Boosting based text and non-text region classification (BX, GA), pp. 1–10.

ICDAR-2011-LeeLLYK #detection

AdaBoost for Text Detection in Natural Scene (JJL, PHL, SWL, ALY, CK), pp. 429–434.

ITiCSE-2011-PollockH #learning #multi

Combining multiple pedagogies to boost learning and enthusiasm (LLP, TH), pp. 258–262.

TACAS-2011-CimattiNR #abstraction #lazy evaluation #partial order #reduction

Boosting Lazy Abstraction for SystemC with Partial Order Reduction (AC, IN, MR), pp. 341–356.

ECIR-2011-CaiGWZ #adaptation #ranking

Weight-Based Boosting Model for Cross-Domain Relevance Ranking Adaptation (PC, WG, KFW, AZ), pp. 562–567.

ICML-2011-GaoK #multi

Multiclass Boosting with Hinge Loss based on Output Coding (TG, DK), pp. 569–576.

ICML-2011-GrubbB #algorithm #optimisation

Generalized Boosting Algorithms for Convex Optimization (AG, DB), pp. 1209–1216.

ICML-2011-Reyzin #predict

Boosting on a Budget: Sampling for Feature-Efficient Prediction (LR), pp. 529–536.

MLDM-2011-ShengT #process

Boosting Inspired Process for Improving AUC (VSS, RT), pp. 199–209.

RecSys-2011-ForbesZ #matrix #recommendation

Content-boosted matrix factorization for recommender systems: experiments with recipe recommendation (PF, MZ), pp. 261–264.

RecSys-2011-KatzOSRS #collaboration #using #wiki

Using Wikipedia to boost collaborative filtering techniques (GK, NO, BS, LR, GS), pp. 285–288.

SIGIR-2011-GanjisaffarCL #modelling #precise #ranking

Bagging gradient-boosted trees for high precision, low variance ranking models (YG, RC, CVL), pp. 85–94.

SIGIR-2011-LvZC #approach #feedback #pseudo

A boosting approach to improving pseudo-relevance feedback (YL, CZ, WC), pp. 165–174.

ESEC-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.

ECSA-2010-ElorantaK #architecture #evaluation #using

Using Domain Knowledge to Boost Software Architecture Evaluation (VPE, KK), pp. 319–326.

SIGMOD-2010-EmrichKKRZ

Boosting spatial pruning: on optimal pruning of MBRs (TE, HPK, PK, MR, AZ), pp. 39–50.

VLDB-2010-HayRMS #consistency

Boosting the Accuracy of Differentially Private Histograms Through Consistency (MH, VR, GM, DS), pp. 1021–1032.

CIKM-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.

CIKM-2010-PavlovGB #hybrid #named #scalability

BagBoo: a scalable hybrid bagging-the-boosting model (DYP, AG, CAB), pp. 1897–1900.

CIKM-2010-TianHZLL #network #social

Boosting social network connectivity with link revival (YT, QH, QZ, XL, WCL), pp. 589–598.

ICML-2010-Busa-FeketeK #performance #using

Fast boosting using adversarial bandits (RBF, BK), pp. 143–150.

ICML-2010-GoldbergE #classification

Boosting Classifiers with Tightened L0-Relaxation Penalties (NG, JE), pp. 383–390.

ICML-2010-GrubbB #composition #learning #network

Boosted Backpropagation Learning for Training Deep Modular Networks (AG, JAB), pp. 407–414.

ICML-2010-PardoeS

Boosting for Regression Transfer (DP, PS), pp. 863–870.

ICPR-2010-BattiatoFGMR

Boosting Gray Codes for Red Eyes Removal (SB, GMF, MG, GM, DR), pp. 4214–4217.

ICPR-2010-FahnC #algorithm #realtime #recognition

Real-Time Upper-Limbs Posture Recognition Based on Particle Filters and AdaBoost Algorithms (CSF, SLC), pp. 3854–3857.

ICPR-2010-HongCCG #detection #set

Boosted Sigma Set for Pedestrian Detection (XH, HC, XC, WG), pp. 3017–3020.

ICPR-2010-HuoF #online #recognition #video

Online Boosting OC for Face Recognition in Continuous Video Stream (HH, JF), pp. 1233–1236.

ICPR-2010-JhuoL #kernel #learning #multi #recognition

Boosted Multiple Kernel Learning for Scene Category Recognition (IHJ, DTL), pp. 3504–3507.

ICPR-2010-JinHL #multi

Multi-class AdaBoost with Hypothesis Margin (XJ, XH, CLL), pp. 65–68.

ICPR-2010-LechervyGP #interactive #retrieval

Active Boosting for Interactive Object Retrieval (AL, PHG, FP), pp. 3268–3271.

ICPR-2010-LefakisWGS #detection

Boosted Edge Orientation Histograms for Grasping Point Detection (LL, HW, MPGT, LS), pp. 4072–4076.

ICPR-2010-PiroNNB #classification

Boosting Bayesian MAP Classification (PP, RN, FN, MB), pp. 661–665.

ICPR-2010-RenHHLC #detection

A Sample Pre-mapping Method Enhancing Boosting for Object Detection (HR, XH, CKH, LL, XC), pp. 3005–3008.

ICPR-2010-SilveiraM #image #using

Boosting Alzheimer Disease Diagnosis Using PET Images (MS, JSM), pp. 2556–2559.

ICPR-2010-SzczotFLP #adaptation #classification

Package Boosting for Readaption of Cascaded Classifiers (MS, JF, OL, GP), pp. 552–555.

ICPR-2010-TakalaCP #clustering #network #sequence

Boosting Clusters of Samples for Sequence Matching in Camera Networks (VT, YC, MP), pp. 400–403.

ICPR-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.

ICPR-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.

ICPR-2010-WangHL #analysis #incremental

Boosting Incremental Semi-supervised Discriminant Analysis for Tracking (HW, XH, CLL), pp. 2748–2751.

ICPR-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.

KDD-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.

KDD-2010-FeiH #classification #functional #graph

Boosting with structure information in the functional space: an application to graph classification (HF, JH), pp. 643–652.

KDIR-2010-BalcazarTZ

Filtering Association Rules with Negations on the Basis of Their Confidence Boost (JLB, CT, MEZ), pp. 263–268.

SEKE-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.

HPCA-2010-LeeCC #multi #named #performance

StimulusCache: Boosting performance of chip multiprocessors with excess cache (HL, SC, BRC), pp. 1–12.

DAC-2009-PanKOMC #process

Selective wordline voltage boosting for caches to manage yield under process variations (YP, JK, SO, GM, SWC), pp. 57–62.

ICDAR-2009-HanifP #algorithm #detection #image #locality #using

Text Detection and Localization in Complex Scene Images using Constrained AdaBoost Algorithm (SMH, LP), pp. 1–5.

HCI-NIMT-2009-FahnKW #realtime #recognition

Real-Time Face Tracking and Recognition Based on Particle Filtering and AdaBoosting Techniques (CSF, MJK, KYW), pp. 198–207.

CIKM-2009-BatalH #classification #using

Boosting KNN text classification accuracy by using supervised term weighting schemes (IB, MH), pp. 2041–2044.

CIKM-2009-FernandezL #retrieval #using

Using opinion-based features to boost sentence retrieval (RTF, DEL), pp. 1617–1620.

CIKM-2009-PanCASD #feature model #ranking #using

Feature selection for ranking using boosted trees (FP, TC, DA, FS, GD), pp. 2025–2028.

CIKM-2009-ShenWCSYCL #classification

Exploiting term relationship to boost text classification (DS, JW, BC, JTS, QY, ZC, YL), pp. 1637–1640.

CIKM-2009-YeCCZ #distributed #probability

Stochastic gradient boosted distributed decision trees (JY, JHC, JC, ZZ), pp. 2061–2064.

CIKM-2009-ZhuCZWWC #algorithm #ranking

A general magnitude-preserving boosting algorithm for search ranking (CZ, WC, ZAZ, GW, DW, ZC), pp. 817–826.

ICML-2009-DuchiS

Boosting with structural sparsity (JCD, YS), pp. 297–304.

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

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

ICML-2009-KeglB #classification

Boosting products of base classifiers (BK, RBF), pp. 497–504.

ICML-2009-Li #adaptation #classification #multi #named

ABC-boost: adaptive base class boost for multi-class classification (PL0), pp. 625–632.

ICML-2009-WarmuthV #bibliography #optimisation #perspective #summary #tutorial

Tutorial summary: Survey of boosting from an optimization perspective (MKW, SVNV), p. 15.

KDD-2009-ZhengWLL

Information theoretic regularization for semi-supervised boosting (LZ, SW, YL, CHL), pp. 1017–1026.

MLDM-2009-BarinovaV #named

ODDboost: Incorporating Posterior Estimates into AdaBoost (OB, DV), pp. 178–190.

MLDM-2009-MoedS #classification #performance

Efficient AdaBoost Region Classification (MM, ENS), pp. 123–136.

SAC-2009-ChenJZY #adaptation #performance

Boosting the performance of computing systems through adaptive configuration tuning (HC, GJ, HZ, KY), pp. 1045–1049.

SAC-2009-Xie #predict

Improved AdaBoost.M1 of decision trees with confidence-rated predictions (ZX), pp. 1462–1466.

PPoPP-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.

DRR-2008-XiangTLW #recognition #reliability #using

Achieving high recognition reliability using decision trees and AdaBoost (JX, XT, YL, PSPW), p. 68150.

STOC-2008-KalaiMV #learning #on the

On agnostic boosting and parity learning (ATK, YM, EV), pp. 629–638.

CIKM-2008-BaoYFXSY #social #using

Boosting social annotations using propagation (SB, BY, BF, SX, ZS, YY), pp. 1507–1508.

CIKM-2008-NiXLH #approach #learning

Group-based learning: a boosting approach (WN, JX, HL, YH), pp. 1443–1444.

ICML-2008-HaffariWWMJ

Boosting with incomplete information (GH, YW, SW, GM, FJ), pp. 368–375.

ICML-2008-LoeffFR #approximate #learning #named

ManifoldBoost: stagewise function approximation for fully-, semi- and un-supervised learning (NL, DAF, DR), pp. 600–607.

ICPR-2008-ChariotK #image #online

GPU-boosted online image matching (AC, RK), pp. 1–4.

ICPR-2008-JiangD

Bhattacharyya boosting (YJ, XD), pp. 1–4.

ICPR-2008-LuGZ #independence #interface

Subject-independent brain computer interface through boosting (SL, CG, HZ), pp. 1–4.

ICPR-2008-SeiffertKHN #classification #named #performance

RUSBoost: Improving classification performance when training data is skewed (CS, TMK, JVH, AN), pp. 1–4.

ICPR-2008-ShimosakaMS #process #recognition #robust

Robust indoor activity recognition via boosting (MS, TM, TS), pp. 1–4.

ICPR-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.

ICPR-2008-TsuchiyaF #feature model #using

A method of feature selection using contribution ratio based on boosting (MT, HF), pp. 1–4.

ICPR-2008-VisentiniSF #detection #online

On-line boosted cascade for object detection (IV, LS, GLF), pp. 1–4.

ICPR-2008-WangZ #collaboration #distributed #learning

Collaborative learning by boosting in distributed environments (SW, CZ), pp. 1–4.

ICPR-2008-XieLZHW #image #recognition

Boosted cannabis image recognition (NX, XL, XZ, WH, JZW), pp. 1–4.

ICPR-2008-YamashitaFLK #online

Human tracking based on Soft Decision Feature and online real boosting (TY, HF, SL, MK), pp. 1–4.

ICPR-2008-ZaidiS #algorithm #detection

Confidence rated boosting algorithm for generic object detection (NAZ, DS), pp. 1–4.

ICPR-2008-ZhangLHT #automation #classification

Boosting local feature descriptors for automatic objects classification in traffic scene surveillance (ZZ, ML, KH, TT), pp. 1–4.

KDD-2008-LozanoA #multi

Multi-class cost-sensitive boosting with p-norm loss functions (ACL, NA), pp. 506–514.

RecSys-2008-DingZYZFB #collaboration #fault #predict #statistics

Boosting collaborative filtering based on statistical prediction errors (SD, SZ, QY, XZ, RF, LDB), pp. 3–10.

SIGIR-2008-AminiTG #algorithm #learning #ranking

A boosting algorithm for learning bipartite ranking functions with partially labeled data (MRA, TVT, CG), pp. 99–106.

SAC-2008-BarczakJM #empirical #evaluation

Empirical evaluation of a new structure for AdaBoost (ALCB, MJJ, CHM), pp. 1764–1765.

SAC-2008-SuKZG #classification #collaboration #machine learning #using

Imputation-boosted collaborative filtering using machine learning classifiers (XS, TMK, XZ, RG), pp. 949–950.

PPoPP-2008-HerlihyK #transaction

Transactional boosting: a methodology for highly-concurrent transactional objects (MH, EK), pp. 207–216.

DATE-2007-CabodiNQ #induction #invariant #model checking

Boosting the role of inductive invariants in model checking (GC, SN, SQ), pp. 1319–1324.

DATE-2007-DabrowskiR #interactive

Interactive presentation: Boosting SER test for RF transceivers by simple DSP technique (JD, RR), pp. 719–724.

DATE-2007-FangH #hybrid #performance #satisfiability

A new hybrid solution to boost SAT solver performance (LF, MSH), pp. 1307–1313.

SIGMOD-2007-MiloZV #clustering #topic

Boosting topic-based publish-subscribe systems with dynamic clustering (TM, TZ, EV), pp. 749–760.

ICML-2007-DaiYXY #learning

Boosting for transfer learning (WD, QY, GRX, YY), pp. 193–200.

ICML-2007-GeurtsWd #kernel

Gradient boosting for kernelized output spaces (PG, LW, FdB), pp. 289–296.

ICML-2007-Masnadi-ShiraziV #symmetry

Asymmetric boosting (HMS, NV), pp. 609–619.

KDD-2007-LiuJJ #clustering #constraints #named

BoostCluster: boosting clustering by pairwise constraints (YL, RJ, AKJ), pp. 450–459.

KDD-2007-YanTS #classification #multi

Model-shared subspace boosting for multi-label classification (RY, JT, JRS), pp. 834–843.

SIGIR-2007-BlancoB

Boosting static pruning of inverted files (RB, AB), pp. 777–778.

SIGIR-2007-XuL #algorithm #information retrieval #named

AdaRank: a boosting algorithm for information retrieval (JX, HL), pp. 391–398.

SAC-2007-TanC #classification #using

Using hypothesis margin to boost centroid text classifier (ST, XC), pp. 398–403.

CIKM-2006-EguchiC #dependence #performance #query

Boosting relevance model performance with query term dependence (KE, WBC), pp. 792–793.

ICML-2006-Li #clustering #multi

Multiclass boosting with repartitioning (LL), pp. 569–576.

ICML-2006-ReyzinS #classification #complexity #how

How boosting the margin can also boost classifier complexity (LR, RES), pp. 753–760.

ICML-2006-WarmuthLR #algorithm

Totally corrective boosting algorithms that maximize the margin (MKW, JL, GR), pp. 1001–1008.

ICPR-v1-2006-ChengZS #detection

Boosted Gabor Features Applied to Vehicle Detection (HC, NZ, CS), pp. 662–666.

ICPR-v1-2006-FuCLR #classification #feature model #image

Boosted Band Ratio Feature Selection for Hyperspectral Image Classification (ZF, TC, NL, ARK), pp. 1059–1062.

ICPR-v1-2006-GaoW #random #recognition

Boosting in Random Subspaces for Face Recognition (YG, YW), pp. 519–522.

ICPR-v1-2006-LinWZZ #optimisation

Continuous Optimization based-on Boosting Gaussian Mixture Model (BL, XW, RtZ, ZZ), pp. 1192–1195.

ICPR-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.

ICPR-v1-2006-WangS06a #component #constraints #detection #geometry

Facial Components Detection with Boosting and Geometric Constraints (TW, PS), pp. 446–449.

ICPR-v1-2006-ZhouW06a #using #verification

Face Verification Using GaborWavelets and AdaBoost (MZ, HW), pp. 404–407.

ICPR-v2-2006-ChenSYCG #detection

Modification of the AdaBoost-based Detector for Partially Occluded Faces (JC, SS, SY, XC, WG), pp. 516–519.

ICPR-v2-2006-DengHG #algorithm #classification

Ada-Boost Algorithm, Classification, Naïve- (WD, JH, JG), pp. 699–702.

ICPR-v2-2006-LeS #detection #named #robust #using

Ent-Boost: Boosting Using Entropy Measure for Robust Object Detection (DDL, SS), pp. 602–605.

ICPR-v2-2006-NiuSYCG #2d #locality

2D Cascaded AdaBoost for Eye Localization (ZN, SS, SY, XC, WG), pp. 1216–1219.

ICPR-v2-2006-RamalhoM #detection #image #using

Using Boosting to Improve Oil Spill Detection in SAR Images (GLBR, FNSdM), pp. 1066–1069.

ICPR-v2-2006-TerradesTV #adaptation #using

Combination of shape descriptors using an adaptation of boosting (ORT, ST, EV), pp. 764–767.

ICPR-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.

ICPR-v3-2006-WangGZLZ #identification #using

Palmprint Identification using Boosting Local Binary Pattern (XW, HG, HZ, BL, ZZ), pp. 503–506.

ICPR-v3-2006-XueD #2d #3d #locality #multi #using

3D+2D Face Localization Using Boosting in Multi-Modal Feature Space (FX, XD), pp. 499–502.

ICPR-v4-2006-EscaleraPR #fault

Forest Extension of Error Correcting Output Codes and Boosted Landmarks (SE, OP, PR), pp. 104–107.

ICPR-v4-2006-LeiDW #adaptation #embedded

AdaBoost Tracker Embedded in Adaptive Particle Filtering (YL, XD, SW), pp. 939–943.

ICDAR-2005-BargeronVS #detection #learning

Boosting-based Transductive Learning for Text Detection (DB, PAV, PYS), pp. 1166–1171.

SIGMOD-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.

ICEIS-v4-2005-DavulcuNR #semantic gap

Boosting Item Findability: Bridging the Semantic Gap between Search Phrases and Item Information (HD, HVN, VR), pp. 48–55.

CIKM-2005-VuG #retrieval #using

Using RankBoost to compare retrieval systems (HTV, PG), pp. 309–310.

ECIR-2005-MishneR #query #retrieval #web

Boosting Web Retrieval Through Query Operations (GM, MdR), pp. 502–516.

ICML-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.

ICML-2005-JinZ #algorithm #probability #using

A smoothed boosting algorithm using probabilistic output codes (RJ, JZ), pp. 361–368.

ICML-2005-SunTLW #framework

Unifying the error-correcting and output-code AdaBoost within the margin framework (YS, ST, JL, DW), pp. 872–879.

KDD-2005-Momma #kernel #performance #scalability

Efficient computations via scalable sparse kernel partial least squares and boosted latent features (MM), pp. 654–659.

KDD-2005-Rosset #robust

Robust boosting and its relation to bagging (SR), pp. 249–255.

MLDM-2005-SunWW #algorithm #parametricity

Parameter Inference of Cost-Sensitive Boosting Algorithms (YS, AKCW, YW), pp. 21–30.

SIGIR-2005-HoweRM #documentation #recognition #retrieval #word

Boosted decision trees for word recognition in handwritten document retrieval (NRH, TMR, RM), pp. 377–383.

CC-2005-JiangMHLZZZ #multi #performance #using

Boosting the Performance of Multimedia Applications Using SIMD Instructions (WJ, CM, BH, JL, JZ, BZ, CZ), pp. 59–75.

DATE-v2-2004-KahngMR #named

Boosting: Min-Cut Placement with Improved Signal Delay (ABK, ILM, SR), pp. 1098–1103.

STOC-2004-GuptaPRS #algorithm #approximate #optimisation #probability

Boosted sampling: approximation algorithms for stochastic optimization (AG, MP, RR, AS), pp. 417–426.

ICEIS-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.

ICML-2004-DietterichAB #random

Training conditional random fields via gradient tree boosting (TGD, AA, YB).

ICML-2004-HertzBW #clustering #distance

Boosting margin based distance functions for clustering (TH, ABH, DW).

ICML-2004-JanodetNSS #grammar inference

Boosting grammatical inference with confidence oracles (JCJ, RN, MS, HMS).

ICML-2004-ZhangKY #algorithm

Surrogate maximization/minimization algorithms for AdaBoost and the logistic regression model (ZZ, JTK, DYY).

ICPR-v1-2004-HeLZZ #classification #image #web

W-Boost and Its Application to Web Image Classification (JH, ML, HZ, CZ), pp. 148–151.

ICPR-v1-2004-SochmanM

Inter-Stage Feature Propagation in Cascade Building with AdaBoost (JS, JM), pp. 236–239.

ICPR-v1-2004-WuAL #detection

Glasses Detection by Boosting Simple Wavelet Features (BW, HA, RL), pp. 292–295.

ICPR-v2-2004-HuangAWL #detection #multi

Boosting Nested Cascade Detector for Multi-View Face Detection (CH, HA, BW, SL), pp. 415–418.

ICPR-v2-2004-ZhangLG #detection #realtime #using

Real-Time Face Detection Using Boosting in Hierarchical Feature Spaces (DZ, SZL, DGP), pp. 411–414.

ICPR-v3-2004-HaugerW #multi

ECOC and Boosting with Multi-Layer Perceptrons (SH, TW), pp. 458–461.

ICPR-v4-2004-WuTR04a

Resolution Enhancement by AdaBoost (JW, MMT, BDR), pp. 893–896.

KDD-2004-BiZB #kernel

Column-generation boosting methods for mixture of kernels (JB, TZ, KPB), pp. 521–526.

DRR-2003-LiuDF

AdaBoost-based handwritten/printed discrimination on a single character (HL, XD, CF), pp. 39–46.

DRR-2003-RossetZH

Boosting and support vector machines as optimal separators (SR, JZ, TH), pp. 1–7.

SCAM-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–?.

STOC-2003-KalaiS

Boosting in the presence of noise (AK, RAS), pp. 195–205.

CIKM-2003-ShanahanR #classification

Boosting support vector machines for text classification through parameter-free threshold relaxation (JGS, NR), pp. 247–254.

CIKM-2003-ZhangOR #learning #using

Learning cross-document structural relationships using boosting (ZZ, JO, DRR), pp. 124–130.

ECIR-2003-NardielloSS #categorisation

Discretizing Continuous Attributes in AdaBoost for Text Categorization (PN, FS, AS), pp. 320–334.

ICML-2003-FernB #lazy evaluation

Boosting Lazy Decision Trees (XZF, CEB), pp. 178–185.

ICML-2003-LeskovecS #dataset #linear #programming

Linear Programming Boosting for Uneven Datasets (JL, JST), pp. 456–463.

ICML-2003-ZhangY #convergence #on the

On the Convergence of Boosting Procedures (TZ, BY), pp. 904–911.

SIGIR-2003-CaiH #automation #categorisation #concept

Text categorization by boosting automatically extracted concepts (LC, TH), pp. 182–189.

CIKM-2002-LiuYC #bias #classification #induction

Boosting to correct inductive bias in text classification (YL, YY, JGC), pp. 348–355.

ICML-2002-MeyerB #scalability #speech #towards

Towards “Large Margin” Speech Recognizers by Boosting and Discriminative Training (CM, PB), pp. 419–426.

ICML-2002-ParkZ #learning

A Boosted Maximum Entropy Model for Learning Text Chunking (SBP, BTZ), pp. 482–489.

ICML-2002-SchapireRRG #information management

Incorporating Prior Knowledge into Boosting (RES, MR, MGR, NKG), pp. 538–545.

ICML-2002-SchapireSMLC #estimation #modelling #nondeterminism #using

Modeling Auction Price Uncertainty Using Boosting-based Conditional Density Estimation (RES, PS, DAM, MLL, JAC), pp. 546–553.

ICML-2002-Zhang #behaviour #consistency #statistics

Statistical Behavior and Consistency of Support Vector Machines, Boosting, and Beyond (TZ0), pp. 690–700.

ICPR-v2-2002-GrimPS #network #probability

Boosting in Probabilistic Neural Networks (JG, PP, PS), pp. 136–139.

ICPR-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–?.

KDD-2002-BennettME #algorithm #kernel #modelling #named

MARK: a boosting algorithm for heterogeneous kernel models (KPB, MM, MJE), pp. 24–31.

KDD-2002-JoshiAK #predict #question

Predicting rare classes: can boosting make any weak learner strong? (MVJ, RCA, VK), pp. 297–306.

ICML-2001-Codrington

Boosting with Confidence Information (CWC), pp. 59–65.

ICML-2001-Das #feature model #hybrid

Filters, Wrappers and a Boosting-Based Hybrid for Feature Selection (SD), pp. 74–81.

ICML-2001-Jiang #aspect-oriented #semistructured data

Some Theoretical Aspects of Boosting in the Presence of Noisy Data (WJ), pp. 234–241.

ICML-2001-KriegerLW #semistructured data

Boosting Noisy Data (AK, CL, AJW), pp. 274–281.

ICML-2001-SebbanNL #classification

Boosting Neighborhood-Based Classifiers (MS, RN, SL), pp. 505–512.

KDD-2001-LazarevicO #algorithm #distributed

The distributed boosting algorithm (AL, ZO), pp. 311–316.

KDD-2001-OzaR #online

Experimental comparisons of online and batch versions of bagging and boosting (NCO, SJR), pp. 359–364.

CIKM-2000-IyerLSSS #documentation

Boosting for Document Routing (RDI, DDL, RES, YS, AS), pp. 70–77.

CIKM-2000-SebastianiSV #algorithm #categorisation

An Improved Boosting Algorithm and its Application to Text Categorization (FS, AS, NV), pp. 78–85.

ICML-2000-BennetDS #algorithm #generative

A Column Generation Algorithm For Boosting (KPB, AD, JST), pp. 65–72.

ICML-2000-Mitchell

“Boosting” a Positive-Data-Only Learner (ARM), pp. 607–614.

ICML-2000-MyersKSW #approach #topic

A Boosting Approach to Topic Spotting on Subdialogues (KM, MJK, SPS, MAW), pp. 655–662.

ICML-2000-NomotoM #analysis #automation

Comparing the Minimum Description Length Principle and Boosting in the Automatic Analysis of Discourse (TN, YM), pp. 687–694.

ICML-2000-OSullivanLCB #algorithm #named #robust

FeatureBoost: A Meta-Learning Algorithm that Improves Model Robustness (JO, JL, RC, AB), pp. 703–710.

ICML-2000-Ting #algorithm #case study #comparative

A Comparative Study of Cost-Sensitive Boosting Algorithms (KMT), pp. 983–990.

ICPR-v2-2000-PavlovMD #algorithm #using

Scaling-Up Support Vector Machines Using Boosting Algorithm (DP, JM, BD), pp. 2219–2222.

SIGIR-2000-KimHZ #classification #naive bayes

Text filtering by boosting naive bayes classifiers (YHK, SYH, BTZ), pp. 168–175.

ICML-1999-FanSZC #classification #named

AdaCost: Misclassification Cost-Sensitive Boosting (WF, SJS, JZ, PKC), pp. 97–105.

ICML-1999-Harries

Boosting a Strong Learner: Evidence Against the Minimum Margin (MBH), pp. 171–180.

ICML-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.

KDD-1999-FanSZ #distributed #learning #online #scalability

The Application of AdaBoost for Distributed, Scalable and On-Line Learning (WF, SJS, JZ), pp. 362–366.

ICML-1998-AbeM #learning #query #using

Query Learning Strategies Using Boosting and Bagging (NA, HM), pp. 1–9.

ICML-1998-FreundISS #algorithm #performance

An Efficient Boosting Algorithm for Combining Preferences (YF, RDI, RES, YS), pp. 170–178.

KDD-1998-RidgewayMRO #classification #naive bayes

Interpretable Boosted Naïve Bayes Classification (GR, DM, TR, JO), pp. 101–104.

SIGIR-1998-SchapireSS

Boosting and Rocchio Applied to Text Filtering (RES, YS, AS), pp. 215–223.

ICML-1997-Drucker #using

Improving Regressors using Boosting Techniques (HD), pp. 107–115.

ICML-1997-MargineantuD #adaptation

Pruning Adaptive Boosting (DDM, TGD), pp. 211–218.

ICML-1997-Schapire #learning #multi #problem #using

Using output codes to boost multiclass learning problems (RES), pp. 313–321.

ICML-1997-SchapireFBL #effectiveness

Boosting the margin: A new explanation for the effectiveness of voting methods (RES, YF, PB, WSL), pp. 322–330.

HPDC-1997-FriedmanR #performance #protocol

Packing Messages as a Tool for Boosting the Performance of Total Ordering Protocols (RF, RvR), pp. 233–242.

STOC-1996-KearnsM #algorithm #learning #on the #top-down

On the Boosting Ability of Top-Down Decision Tree Learning Algorithms (MJK, YM), pp. 459–468.

ICML-1996-FreundS #algorithm

Experiments with a New Boosting Algorithm (YF, RES), pp. 148–156.

JICSLP-1996-LhommeGRT #algorithm

Boosting the Interval Narrowing Algorithm (OL, AG, MR, PT), pp. 378–392.

ICML-1994-DruckerCJCV #algorithm #machine learning

Boosting and Other Machine Learning Algorithms (HD, CC, LDJ, YL, VV), pp. 53–61.

ASPLOS-1992-SmithHL #performance

Efficient Superscalar Performance Through Boosting (MDS, MH, MSL), pp. 248–259.

ICLP-1987-Gallaire87 #logic programming

Boosting Logic Programming (HG), pp. 962–988.