Stem margin$ (all stems)

143 papers:

DATE-2015-ChungSS #identification

Identifying redundant inter-cell margins and its application to reducing routing congestion (WC, SS, YS), pp. 1659–1664.

DATE-2015-ConstantinWKCB

Exploiting dynamic timing margins in microprocessors for frequency-over-scaling with instruction-based clock adjustment (JC, LW, GK, AC, AB), pp. 381–386.

DATE-2015-GomezPBRBFG #design #energy

Reducing energy consumption in microcontroller-based platforms with low design margin co-processors (AG, CP, AB, DR, LB, HF, JPdG), pp. 269–272.

DLT-2015-HanKS #fault

Generalizations of Code Languages with Marginal Errors (YSH, SKK, KS), pp. 264–275.

ICML-2015-HayashiMF

Rebuilding Factorized Information Criterion: Asymptotically Accurate Marginal Likelihood (KH, SiM, RF), pp. 1358–1366.

ICML-2015-PachecoS #approach #pseudo

Proteins, Particles, and Pseudo-Max-Marginals: A Submodular Approach (JP, EBS), pp. 2200–2208.

ICML-2015-YuanHTLC #modelling

Non-Gaussian Discriminative Factor Models via the Max-Margin Rank-Likelihood (XY, RH, ET, RL, LC), pp. 1254–1263.

SIGIR-2015-XiaXLGC #evaluation #learning #metric #optimisation

Learning Maximal Marginal Relevance Model via Directly Optimizing Diversity Evaluation Measures (LX, JX, YL, JG, XC), pp. 113–122.

DATE-2014-0001GWKAWG #optimisation #performance #runtime

Exploiting expendable process-margins in DRAMs for run-time performance optimization (KC, SG, CW, MK, BA, NW, KG), pp. 1–6.

SIGMOD-2014-QardajiYL #named

PriView: practical differentially private release of marginal contingency tables (WHQ, WY, NL), pp. 1435–1446.

CHI-2014-DachteraRW #design #industrial #research

Research on research: design research at the margins: academia, industry and end-users (JD, DWR, VW), pp. 713–722.

CHI-2014-HoareBJM #online

Coming in from the margins: amateur musicians in the online age (MH, SB, RJ, NMF), pp. 1295–1304.

ICML-c1-2014-LajugieBA #clustering #learning #metric #problem

Large-Margin Metric Learning for Constrained Partitioning Problems (RL, FRB, SA), pp. 297–305.

ICML-c1-2014-RamdasP #kernel

Margins, Kernels and Non-linear Smoothed Perceptrons (AR, JP), pp. 244–252.

ICML-c1-2014-ZhangZZ #infinity #markov #modelling

Max-Margin Infinite Hidden Markov Models (AZ, JZ, BZ), pp. 315–323.

ICML-c2-2014-ChenWSB

Marginalized Denoising Auto-encoders for Nonlinear Representations (MC, KQW, FS, YB), pp. 1476–1484.

ICML-c2-2014-KarninH #linear

Hard-Margin Active Linear Regression (ZSK, EH), pp. 883–891.

ICML-c2-2014-KontorovichW #multi #nearest neighbour

Maximum Margin Multiclass Nearest Neighbors (AK, RW), pp. 892–900.

ICML-c2-2014-LiZ0 #learning #multi

Bayesian Max-margin Multi-Task Learning with Data Augmentation (CL, JZ, JC), pp. 415–423.

ICML-c2-2014-PingLI

Marginal Structured SVM with Hidden Variables (WP, QL, ATI), pp. 190–198.

ICML-c2-2014-QuattoniBCG #sequence

Spectral Regularization for Max-Margin Sequence Tagging (AQ, BB, XC, AG), pp. 1710–1718.

ICML-c2-2014-XuTXR #reduction

Large-margin Weakly Supervised Dimensionality Reduction (CX, DT, CX, YR), pp. 865–873.

ICPR-2014-Filippone #classification #process #pseudo

Bayesian Inference for Gaussian Process Classifiers with Annealing and Pseudo-Marginal MCMC (MF), pp. 614–619.

ICPR-2014-Jain #graph

Margin Perceptrons for Graphs (BJJ), pp. 3851–3856.

ICPR-2014-NguyenTHM #classification #novel

A Novel Sphere-Based Maximum Margin Classification Method (PN, DT, XH, WM), pp. 620–624.

ICPR-2014-OHarneyMRCSCBF #kernel #learning #multi #pseudo

Pseudo-Marginal Bayesian Multiple-Class Multiple-Kernel Learning for Neuroimaging Data (ADO, AM, KR, KC, ABS, AC, CB, MF), pp. 3185–3190.

ICPR-2014-WangGJ #learning #using

Learning with Hidden Information Using a Max-Margin Latent Variable Model (ZW, TG, QJ), pp. 1389–1394.

KDD-2014-ZhangZ #scalability

Large margin distribution machine (TZ, ZHZ), pp. 313–322.

HIMI-HSM-2013-KarashimaN #behaviour #safety

Influence of the Safety Margin on Behavior that Violates Rules (MK, HN), pp. 497–506.

ICML-c1-2013-DoK

Convex formulations of radius-margin based Support Vector Machines (HD, AK), pp. 169–177.

ICML-c1-2013-MaatenCTW #learning

Learning with Marginalized Corrupted Features (LvdM, MC, ST, KQW), pp. 410–418.

ICML-c1-2013-ZhuCPZ #algorithm #modelling #performance #topic

Gibbs Max-Margin Topic Models with Fast Sampling Algorithms (JZ, NC, HP, BZ), pp. 124–132.

ICML-c2-2013-Telgarsky

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

ICML-c3-2013-BalasubramanianYL #learning

Smooth Sparse Coding via Marginal Regression for Learning Sparse Representations (KB, KY, GL), pp. 289–297.

ICML-c3-2013-CortesMR #classification #kernel #multi

Multi-Class Classification with Maximum Margin Multiple Kernel (CC, MM, AR), pp. 46–54.

ICML-c3-2013-HockingRVB #detection #learning #using

Learning Sparse Penalties for Change-point Detection using Max Margin Interval Regression (TH, GR, JPV, FRB), pp. 172–180.

ICML-c3-2013-PeharzTP #generative #network

The Most Generative Maximum Margin Bayesian Networks (RP, ST, FP), pp. 235–243.

ICML-c3-2013-WangWBLT #learning #multi #taxonomy

Max-Margin Multiple-Instance Dictionary Learning (XW, BW, XB, WL, ZT), pp. 846–854.

ICML-c3-2013-XuZZ #matrix #performance

Fast Max-Margin Matrix Factorization with Data Augmentation (MX, JZ, BZ), pp. 978–986.

KDD-2013-ZhuZZZ #modelling #scalability #topic

Scalable inference in max-margin topic models (JZ, XZ, LZ, BZ), pp. 964–972.

SIGIR-2013-LuWTZHZ #rank #ranking #scalability

A low rank structural large margin method for cross-modal ranking (XL, FW, ST, ZZ, XH, YZ), pp. 433–442.

CASE-2012-JeongC #algorithm #independence #quality

Independent contact region (ICR) based in-hand motion planning algorithm with guaranteed grasp quality margin (HJ, JC), pp. 1089–1094.

ICALP-v1-2012-ThalerUV #algorithm #performance

Faster Algorithms for Privately Releasing Marginals (JT, JU, SPV), pp. 810–821.

CIKM-2012-FanZCCO #clustering

Maximum margin clustering on evolutionary data (XF, LZ, LC, XC, YSO), pp. 625–634.

CIKM-2012-XuCWS #representation

From sBoW to dCoT marginalized encoders for text representation (ZEX, MC, KQW, FS), pp. 1879–1884.

ICML-2012-ChenXWS #adaptation

Marginalized Denoising Autoencoders for Domain Adaptation (MC, ZEX, KQW, FS), p. 212.

ICML-2012-MauaC

Anytime Marginal MAP Inference (DDM, CPdC), p. 181.

ICML-2012-PeharzP #learning #network

Exact Maximum Margin Structure Learning of Bayesian Networks (RP, FP), p. 102.

ICML-2012-ShiSHH #question #random

Is margin preserved after random projection? (QS, CS, RH, AvdH), p. 86.

ICML-2012-ZhangS

Maximum Margin Output Coding (YZ, JGS), p. 53.

ICML-2012-Zhu #feature model #modelling #parametricity #predict

Max-Margin Nonparametric Latent Feature Models for Link Prediction (JZ), p. 154.

ICPR-2012-ChenYY #analysis #null #recognition #scalability

Large margin null space discriminant analysis with applications to face recognition (XC, JY, WY), pp. 1679–1682.

ICPR-2012-SuDRH

Hypergraph matching based on Marginalized Constrained Compatibility (JS, LD, PR, ERH), pp. 2922–2925.

KDD-2012-ChattopadhyayWFDPY #probability

Batch mode active sampling based on marginal probability distribution matching (RC, ZW, WF, ID, SP, JY), pp. 741–749.

CASE-2011-LeH #analysis #random

Marginal analysis on binary pairwise Gibbs random fields (TL, CNH), pp. 316–321.

CIKM-2011-ZhaoYX #independence #information management #learning #web

Max margin learning on domain-independent web information extraction (BZ, XY, EPX), pp. 1305–1310.

ICML-2011-Gould #learning #linear #markov #random

Max-margin Learning for Lower Linear Envelope Potentials in Binary Markov Random Fields (SG), pp. 193–200.

ICML-2011-KamisettyXL #approximate #correlation #using

Approximating Correlated Equilibria using Relaxations on the Marginal Polytope (HK, EPX, CJL), pp. 1153–1160.

ICML-2011-ZhuCX #infinity #kernel #process

Infinite SVM: a Dirichlet Process Mixture of Large-margin Kernel Machines (JZ, NC, EPX), pp. 617–624.

KDD-2011-FujimakiSM #modelling #online

Online heterogeneous mixture modeling with marginal and copula selection (RF, YS, SM), pp. 645–653.

KDD-2011-ZhangHLSL #approach #learning #multi #scalability

Multi-view transfer learning with a large margin approach (DZ, JH, YL, LS, RDL), pp. 1208–1216.

SIGIR-2011-WangZ #e-commerce #recommendation

Utilizing marginal net utility for recommendation in e-commerce (JW, YZ), pp. 1003–1012.

ECIR-2010-AgarwalC #algorithm #information retrieval #ranking

Maximum Margin Ranking Algorithms for Information Retrieval (SA, MC), pp. 332–343.

ICML-2010-HariharanZVV #classification #multi #scalability

Large Scale Max-Margin Multi-Label Classification with Priors (BH, LZM, SVNV, MV), pp. 423–430.

ICML-2010-PanagiotakopoulosT

The Margin Perceptron with Unlearning (CP, PT), pp. 855–862.

ICPR-2010-CevikalpY #classification #scalability

Large Margin Classifier Based on Affine Hulls (HC, HSY), pp. 21–24.

ICPR-2010-ChenS #classification #nearest neighbour #scalability

Hierarchical Large Margin Nearest Neighbor Classification (QC, SS), pp. 906–909.

ICPR-2010-GriptonL #kernel #semistructured data #using

Kernel Domain Description with Incomplete Data: Using Instance-Specific Margins to Avoid Imputation (AG, WL), pp. 2921–2924.

ICPR-2010-JinHL #multi

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

ICPR-2010-ShibataKI #classification #nearest neighbour #performance #scalability

Large Margin Discriminant Hashing for Fast k-Nearest Neighbor Classification (TS, SK, SI), pp. 1015–1018.

ICPR-2010-TakahashiK #approximate #classification

Margin Preserved Approximate Convex Hulls for Classification (TT, MK), pp. 4052–4055.

SIGIR-2010-GuoS #probability

Probabilistic latent maximal marginal relevance (SG, SS), pp. 833–834.

DAC-2009-DasBBFA #design

Addressing design margins through error-tolerant circuits (SD, DB, DMB, KF, RA), pp. 11–12.

ICDAR-2009-DoA #recognition

Maximum Margin Training of Gaussian HMMs for Handwriting Recognition (TMTD, TA), pp. 976–980.

CIKM-2009-GuZ #clustering

Subspace maximum margin clustering (QG, JZ), pp. 1337–1346.

CIKM-2009-KurasawaFTA #clustering #metric #similarity

Maximal metric margin partitioning for similarity search indexes (HK, DF, AT, JA), pp. 1887–1890.

CIKM-2009-QuanzH #learning #scalability

Large margin transductive transfer learning (BQ, JH), pp. 1327–1336.

ICML-2009-DoA #markov #modelling #scalability

Large margin training for hidden Markov models with partially observed states (TMTD, TA), pp. 265–272.

ICML-2009-GiesekePK #clustering #performance

Fast evolutionary maximum margin clustering (FG, TP, OK), pp. 361–368.

ICML-2009-ZhuAX #classification #modelling #named #topic

MedLDA: maximum margin supervised topic models for regression and classification (JZ, AA, EPX), pp. 1257–1264.

KDD-2009-ZhuXZ #markov #network

Primal sparse Max-margin Markov networks (JZ, EPX, BZ), pp. 1047–1056.

MLDM-2009-LiuYZZZL #classification #scalability

A Large Margin Classifier with Additional Features (XL, JY, EZ, GZ, YZ, ML), pp. 82–95.

RecSys-2009-WeimerKB #matrix #recommendation

Maximum margin matrix factorization for code recommendation (MW, AK, MB), pp. 309–312.

HPCA-2009-ReddiGHWSB #predict #using

Voltage emergency prediction: Using signatures to reduce operating margins (VJR, MSG, GHH, GYW, MDS, DMB), pp. 18–29.

CASE-2008-Pohjola #adaptation

Adaptive jitter margin PID controller (MP), pp. 534–539.

DAC-2008-SenNSC #adaptation #named #power management #process

Pro-VIZOR: process tunable virtually zero margin low power adaptive RF for wireless systems (SS, VN, RS, AC), pp. 492–497.

DATE-2008-XiongZVH

Optimal Margin Computation for At-Speed Test (JX, VZ, CV, PAH), pp. 622–627.

CIKM-2008-WangCZL #constraints #learning #metric

Semi-supervised metric learning by maximizing constraint margin (FW, SC, CZ, TL), pp. 1457–1458.

ICML-2008-HeigoldDSN #evaluation #recognition #speech

Modified MMI/MPE: a direct evaluation of the margin in speech recognition (GH, TD, RS, HN), pp. 384–391.

ICML-2008-SarawagiG

Accurate max-margin training for structured output spaces (SS, RG), pp. 888–895.

ICML-2008-ZhaoWZ #clustering #multi #performance

Efficient multiclass maximum margin clustering (BZ, FW, CZ), pp. 1248–1255.

ICML-2008-ZhuXZ #markov #network

Laplace maximum margin Markov networks (JZ, EPX, BZ), pp. 1256–1263.

ICPR-2008-JinLH #learning #prototype

Prototype learning with margin-based conditional log-likelihood loss (XJ, CLL, XH), pp. 1–4.

ICPR-2008-YangWRY #feature model

Feature Extraction base on Local Maximum Margin Criterion (WY, JW, MR, JY), pp. 1–4.

RE-2008-SimAA #experience #requirements #what

Marginal Notes on Amethodical Requirements Engineering: What Experts Learned from Experience (SES, TAA, BAA), pp. 105–114.

DATE-2007-AitkenI #design #embedded #worst-case

Worst-case design and margin for embedded SRAM (RCA, SI), pp. 1289–1294.

CHI-2007-LjungbladH

Transfer scenarios: grounding innovation with marginal practices (SL, LEH), pp. 737–746.

ICML-2007-TsampoukaS #algorithm #approximate

Approximate maximum margin algorithms with rules controlled by the number of mistakes (PT, JST), pp. 903–910.

ICML-2007-ZhangTK #clustering

Maximum margin clustering made practical (KZ, IWT, JTK), pp. 1119–1126.

KDD-2007-GuoZXF #data mining #database #learning #mining #multimodal

Enhanced max margin learning on multimodal data mining in a multimedia database (ZG, ZZ, EPX, CF), pp. 340–349.

SAC-2007-TanC #classification #using

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

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

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

ICML-2006-RatliffBZ

Maximum margin planning (NDR, JAB, MZ), pp. 729–736.

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-AdachiKO #estimation #fault #reliability

Reliability index of optical flow that considers error margin of matches and stabilizes camera movement estimation (EA, TK, NO), pp. 699–702.

ICPR-v2-2006-XuWH #algorithm #learning

A maximum margin discriminative learning algorithm for temporal signals (WX, JW, ZH), pp. 460–463.

ICPR-v3-2006-YoshimuraIY #adaptation #correlation #detection

Object Detection with Adaptive Background Model and Margined Sign Cross Correlation (HY, YI, MY), pp. 19–23.

KDD-2006-NathBM #approach #classification #clustering #scalability #using

Clustering based large margin classification: a scalable approach using SOCP formulation (JSN, CB, MNM), pp. 674–679.

ICML-2005-DaumeM #approximate #learning #optimisation #predict #scalability

Learning as search optimization: approximate large margin methods for structured prediction (HDI, DM), pp. 169–176.

ICML-2005-RennieS #collaboration #matrix #performance #predict

Fast maximum margin matrix factorization for collaborative prediction (JDMR, NS), pp. 713–719.

ICML-2005-SunTLW #framework

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

ICML-2005-TaskarCKG #approach #learning #modelling #predict #scalability

Learning structured prediction models: a large margin approach (BT, VC, DK, CG), pp. 896–903.

ICML-2005-WuSB #classification #scalability

Building Sparse Large Margin Classifiers (MW, BS, GHB), pp. 996–1003.

ICML-2005-ZienC #scalability

Large margin non-linear embedding (AZ, JQC), pp. 1060–1067.

ICML-2004-DekelKS #classification #scalability

Large margin hierarchical classification (OD, JK, YS).

ICML-2004-Gilad-BachrachNT #algorithm #feature model

Margin based feature selection — theory and algorithms (RGB, AN, NT).

ICML-2004-HertzBW #clustering #distance

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

ICML-2004-HuangYKL #classification #learning #scalability

Learning large margin classifiers locally and globally (KH, HY, IK, MRL).

ICML-2004-KrauseS

Leveraging the margin more carefully (NK, YS).

ICML-2004-LebanonL #classification #multi

Hyperplane margin classifiers on the multinomial manifold (GL, JDL).

ICML-2004-MaheUAPV #graph #kernel

Extensions of marginalized graph kernels (PM, NU, TA, JLP, JPV).

KDD-2004-WuS #information management

Incorporating prior knowledge with weighted margin support vector machines (XW, RKS), pp. 326–333.

KDD-2004-YanZYYLCXFMC #incremental #named

IMMC: incremental maximum margin criterion (JY, BZ, SY, QY, HL, ZC, WX, WF, WYM, QC), pp. 725–730.

CIKM-2003-YangK #adaptation

Margin-based local regression for adaptive filtering (YY, BK), pp. 191–198.

ICML-2003-GargR #learning

Margin Distribution and Learning (AG, DR), pp. 210–217.

ICML-2003-KashimaTI #graph #kernel

Marginalized Kernels Between Labeled Graphs (HK, KT, AI), pp. 321–328.

ICML-2003-PorterEHT #classification #order #scalability #statistics

Weighted Order Statistic Classifiers with Large Rank-Order Margin (RBP, DE, DRH, JT), pp. 600–607.

ICML-2002-GargHR #bound #on the

On generalization bounds, projection profile, and margin distribution (AG, SHP, DR), pp. 171–178.

ICML-2002-LiZHSK #algorithm

The Perceptron Algorithm with Uneven Margins (YL, HZ, RH, JST, JSK), pp. 379–386.

ICML-2002-MeyerB #scalability #speech #towards

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

ICDAR-2001-FanLW #documentation #image

Marginal Noise Removal of Document Images (KCF, TRL, YKW), pp. 317–321.

ICML-2000-AllweinSS #approach #classification #multi

Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers (ELA, RES, YS), pp. 9–16.

ICML-2000-CampbellCS #classification #learning #query #scalability

Query Learning with Large Margin Classifiers (CC, NC, AJS), pp. 111–118.

ICML-2000-FiechterR #learning #scalability

Learning Subjective Functions with Large Margins (CNF, SR), pp. 287–294.

HCI-CCAD-1999-Pieper

Information environments to overcome isolation, marginalization and stigmatization in an overaging information society (MP), pp. 883–887.

ICML-1999-Harries

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

ICML-1999-WuBCS #induction #scalability

Large Margin Trees for Induction and Transduction (DW, KPB, NC, JST), pp. 474–483.

ICML-1998-CristianiniSS #classification #scalability

Bayesian Classifiers Are Large Margin Hyperplanes in a Hilbert Space (NC, JST, PS), pp. 109–117.

ICML-1997-SchapireFBL #effectiveness

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

ICPR-1996-RaudysD #classification #empirical #fault

Expected error of minimum empirical error and maximal margin classifiers (SR, VD), pp. 875–879.

PODS-1991-MalvestutoMR #2d #information management #statistics

Suppressing Marginal Cells to Protect Sensitive Information in a Two-Dimensional Statistical Table (FMM, MM, MR), pp. 252–258.

SIGMOD-1991-NgFS #flexibility

Flexible Buffer Allocation Based on Marginal Gains (RTN, CF, TKS), pp. 387–396.