Stem noisi$ (all stems)

115 papers:

PODS-2015-GrozM #query

Skyline Queries with Noisy Comparisons (BG, TM), pp. 185–198.

STOC-2015-LovettZ #difference

Improved Noisy Population Recovery, and Reverse Bonami-Beckner Inequality for Sparse Functions (SL, JZ), pp. 137–142.

ECIR-2015-EfremovaGC #classification

Classification of Historical Notary Acts with Noisy Labels (JE, AMG, TC), pp. 49–54.

ICML-2015-WangWS #analysis #clustering

A Deterministic Analysis of Noisy Sparse Subspace Clustering for Dimensionality-reduced Data (YW, YXW, AS), pp. 1422–1431.

ICML-2015-WuGS #combinator #feedback #finite #identification #on the

On Identifying Good Options under Combinatorially Structured Feedback in Finite Noisy Environments (YW, AG, CS), pp. 1283–1291.

SIGIR-2015-ChakrabortyGP #corpus #retrieval

Retrieval from Noisy E-Discovery Corpus in the Absence of Training Data (AC, KG, SKP), pp. 755–758.

CASE-2014-LeeJMC #framework #identification #recognition #robust

Iterative identification framework for robust hand-written digit recognition under extremely noisy conditions (HL, SJ, TM, NYC), pp. 728–733.

CASE-2014-VaskeviciusP0 #locality #low cost #recognition

Fitting superquadrics in noisy, partial views from a low-cost RGBD sensor for recognition and localization of sacks in autonomous unloading of shipping containers (NV, KP, AB), pp. 255–262.

FoSSaCS-2014-AlmagorK #synthesis

Latticed-LTL Synthesis in the Presence of Noisy Inputs (SA, OK), pp. 226–241.

ICML-c2-2014-WangL #estimation #metric #robust

Robust Inverse Covariance Estimation under Noisy Measurements (JKW, SdL), pp. 928–936.

ICPR-2014-LiZSG #image

Joint Motion Deblurring with Blurred/Noisy Image Pair (HL, YZ, JS, DG), pp. 1020–1024.

ICPR-2014-PinheiroRCJS #fuzzy #independence #robust #verification

Type-2 Fuzzy GMMs for Robust Text-Independent Speaker Verification in Noisy Environments (HNBP, TIR, GDCC, IJT, JS), pp. 4531–4536.

KDIR-2014-ChakrabortyGR #approach #performance #retrieval #word

A Word Association Based Approach for Improving Retrieval Performance from Noisy OCRed Text (AC, KG, UR), pp. 450–456.

ICDAR-2013-GerdjikovMN

Extraction of Spelling Variations from Language Structure for Noisy Text Correction (SG, SM, VN), pp. 324–328.

ICDAR-2013-WemhoenerYM #multi #using

Creating an Improved Version Using Noisy OCR from Multiple Editions (DW, IZY, RM), pp. 160–164.

SIGMOD-2013-LiGC #named #realtime #sequence

ε-Matching: event processing over noisy sequences in real time (ZL, TG, CXC), pp. 601–612.

SIGMOD-2013-MoustafaMDG #analysis #declarative #interactive #named #network

GRDB: a system for declarative and interactive analysis of noisy information networks (WEM, HM, AD, LG), pp. 1085–1088.

HCI-IMT-2013-KuncMLK #speech

Speech-Based Text Correction Patterns in Noisy Environment (LK, TM, ML, JK), pp. 59–66.

CIKM-2013-ChanLKLBR #graph #matrix #using

Discovering latent blockmodels in sparse and noisy graphs using non-negative matrix factorisation (JC, WL, AK, CL, JB, KR), pp. 811–816.

CIKM-2013-RothK #modelling #quality

Feature-based models for improving the quality of noisy training data for relation extraction (BR, DK), pp. 1181–1184.

CIKM-2013-ShirakawaNHN #metric #probability #semantics #similarity #using #wiki

Probabilistic semantic similarity measurements for noisy short texts using Wikipedia entities (MS, KN, TH, SN), pp. 903–908.

ICML-c1-2013-ChenC13a

Noisy and Missing Data Regression: Distribution-Oblivious Support Recovery (YC, CC), pp. 383–391.

ICML-c1-2013-Hennig #optimisation #performance #probability

Fast Probabilistic Optimization from Noisy Gradients (PH), pp. 62–70.

ICML-c1-2013-WangX #clustering

Noisy Sparse Subspace Clustering (YXW, HX), pp. 89–97.

ICML-c3-2013-Busa-FeketeSCWH #adaptation

Top-k Selection based on Adaptive Sampling of Noisy Preferences (RBF, BS, WC, PW, EH), pp. 1094–1102.

SAC-2013-KhaniHAB #algorithm #clustering #semistructured data #set

An algorithm for discovering clusters of different densities or shapes in noisy data sets (FK, MJH, AAA, HB), pp. 144–149.

DRR-2012-OujiLL #documentation #segmentation

Comprehensive color segmentation system for noisy digitized documents to enhance text extraction (AO, YL, FL).

CHI-2012-CasiezRV #interactive

1 € filter: a simple speed-based low-pass filter for noisy input in interactive systems (GC, NR, DV), pp. 2527–2530.

CIKM-2012-QiYZZ #mining #multi

Mining noisy tagging from multi-label space (ZQ, MY, Z(Z, ZZ), pp. 1925–1929.

ICML-2012-MnihH #image #learning #semistructured data

Learning to Label Aerial Images from Noisy Data (VM, GEH), p. 31.

ICML-2012-YangMJZZ #kernel #learning #multi #probability #programming

Multiple Kernel Learning from Noisy Labels by Stochastic Programming (TY, MM, RJ, LZ, YZ), p. 21.

ICPR-2012-CaoCZL #clustering #query

Locating high-density clusters with noisy queries (CC, SC, CZ, JL), pp. 3537–3540.

ICPR-2012-Largeteau-SkapinZASK #set

Optimal consensus set and preimage of 4-connected circles in a noisy environment (GLS, RZ, EA, AS, YK), pp. 3774–3777.

ICPR-2012-MadabusiG #detection #image

Edge detection for facial images under noisy conditions (SM, SVG), pp. 2689–2693.

ICPR-2012-ZhaoSS #learning #predict

Importance-weighted label prediction for active learning with noisy annotations (LZ, GS, RS), pp. 3476–3479.

ICDAR-2011-ChenL #detection #documentation

Table Detection in Noisy Off-line Handwritten Documents (JC, DPL), pp. 399–403.

ICDAR-2011-ChenL11a #algorithm #detection #documentation #modelling

A Model-Based Ruling Line Detection Algorithm for Noisy Handwritten Documents (JC, DPL), pp. 404–408.

ICDAR-2011-OujiLL #documentation #image

Chromatic / Achromatic Separation in Noisy Document Images (AO, YL, FL), pp. 167–171.

FoSSaCS-2011-VelnerR #problem #synthesis

Church Synthesis Problem for Noisy Input (YV, AR), pp. 275–289.

ICEIS-J-2011-Li11f #analysis #approach #case study #machine learning #type system #using

A Study on Noisy Typing Stream Analysis Using Machine Learning Approach (JL0), pp. 149–161.

ICML-2011-AgarwalNW #composition #matrix

Noisy matrix decomposition via convex relaxation: Optimal rates in high dimensions (AA, SN, MJW), pp. 1129–1136.

ICML-2011-ZhouT #composition #matrix #named #random

GoDec: Randomized Lowrank & Sparse Matrix Decomposition in Noisy Case (TZ, DT), pp. 33–40.

SIGIR-2011-KumarL #learning #rank

Learning to rank from a noisy crowd (AK, ML), pp. 1221–1222.

DRR-2010-LiuZ #detection #documentation #image #learning

Semi-supervised learning for detecting text-lines in noisy document images (ZL, HZ), pp. 1–10.

ICML-2010-YangJJ #learning

Learning from Noisy Side Information by Generalized Maximum Entropy Model (TY, RJ, AKJ), pp. 1199–1206.

ICPR-2010-Nolazco-FloresLG #automation #recognition #speech

Speech Magnitude-Spectrum Information-Entropy (MSIE) for Automatic Speech Recognition in Noisy Environments (JANF, RAAL, LPGP), pp. 4364–4367.

ICPR-2010-TawariT #analysis #speech

Speech Emotion Analysis in Noisy Real-World Environment (AT, MMT), pp. 4605–4608.

SAC-2010-ChoePR #estimation

Dispatching AGVs with noisy estimation of crane operation time (RC, TP, KRR), pp. 1288–1293.

DAC-2009-ZhangS #estimation #using

Accurate temperature estimation using noisy thermal sensors (YZ, AS), pp. 472–477.

HT-2009-WangC #analysis #web

Use noisy link analysis to improve web search (YW, JC), pp. 377–378.

ICDAR-2009-LecerfC #documentation #feature model #scalability

Scalable Feature Extraction from Noisy Documents (LL, BC), pp. 361–365.

ICML-2009-DeodharGGCD #clustering #framework #scalability #semistructured data

A scalable framework for discovering coherent co-clusters in noisy data (MD, GG, JG, HC, ISD), pp. 241–248.

ICML-2009-YuilleZ #composition #learning

Compositional noisy-logical learning (ALY, SZ), pp. 1209–1216.

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

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

ICPR-2008-HuynhH #estimation #recursion #sequence #video

Recursive structure and motion estimation from noisy uncalibrated video sequences (DQH, AH), pp. 1–5.

KDD-2008-ShengPI #data mining #mining #multi #quality #using

Get another label? improving data quality and data mining using multiple, noisy labelers (VSS, FJP, PGI), pp. 614–622.

ICDAR-2007-ChenW #documentation #image #kernel

Exploiting Fisher Kernels in Decoding Severely Noisy Document Images (JC, YW), pp. 417–421.

ICML-2007-MylonakisSH #estimation #modelling

Unsupervised estimation for noisy-channel models (MM, KS, RH), pp. 665–672.

ICML-2007-ParkerFT #learning #performance #query #retrieval

Learning for efficient retrieval of structured data with noisy queries (CP, AF, PT), pp. 729–736.

SAC-2007-LiuMB #approach #clustering

A clustering entropy-driven approach for exploring and exploiting noisy functions (SHL, MM, BRB), pp. 738–742.

CIKM-2006-ShiN #adaptation #corpus #information retrieval #parallel

Filtering or adapting: two strategies to exploit noisy parallel corpora for cross-language information retrieval (LS, JYN), pp. 814–815.

ICPR-v1-2006-TongT #3d #multi #re-engineering #set

Multiresolution Mesh Reconstruction from Noisy 3D Point Sets (WST, CKT), pp. 5–8.

ICPR-v2-2006-ZhouBS #image #using

Extracting Lines in Noisy Image Using Directional Information (JZ, WFB, GASA), pp. 215–218.

ICPR-v4-2006-ProencaA #identification #image #normalisation

A Method for the Identification of Noisy Regions in Normalized Iris Images (HP, LAA), pp. 405–408.

DATE-2005-NazarianPTLA #analysis #modelling

Modeling and Propagation of Noisy Waveforms in Static Timing Analysis (SN, MP, ET, TL, AHA), pp. 776–777.

ICALP-2005-AsarinC #turing machine

Noisy Turing Machines (EA, PC), pp. 1031–1042.

ICML-2005-RamakrishnanCKB #approximate #classification

A model for handling approximate, noisy or incomplete labeling in text classification (GR, KPC, RK, PB), pp. 681–688.

SAC-2005-Lopresti #evaluation #performance

Performance evaluation for text processing of noisy inputs (DPL), pp. 759–763.

SIGMOD-2004-QianZZ #approach #clustering #effectiveness #named #performance

FAÇADE: A Fast and Effective Approach to the Discovery of Dense Clusters in Noisy Spatial Data (YQ, GZ, KZ), pp. 921–922.

CIAA-2004-KariKS

Substitutions, Trajectories and Noisy Channels (LK, SK, PS), pp. 202–212.

ICPR-v2-2004-Vinciarelli #categorisation

Noisy Text Categorization (AV), pp. 554–557.

SAC-2004-AvilaL #algorithm #documentation

A new algorithm for removing noisy borders from monochromatic documents (BTÁ, RDL), pp. 1219–1225.

DATE-2003-CaldariCCMGOT #modelling

SystemC Modeling of a Bluetooth Transceiver: Dynamic Management of Packet Type in a Noisy Channel (MC, MC, PC, GM, FDG, SO, CT), pp. 20214–20219.

ICDAR-2003-LucasPD #image #performance #recognition #word

Fast Lexicon-Based Word Recognition in Noisy Index Card Images (SML, GP, ACD), pp. 462–466.

ICDAR-2003-ZhengLD03a #documentation #identification #image #markov #random #using

Text Identification in Noisy Document Images Using Markov Random Field (YZ, HL, DSD), p. 599–?.

STOC-2003-CoppersmithS #higher-order #semistructured data

Reconstructing curves in three (and higher) dimensional space from noisy data (DC, MS), pp. 136–142.

ICALP-2003-BleichenbacherKY #semistructured data

Decoding of Interleaved Reed Solomon Codes over Noisy Data (DB, AK, MY), pp. 97–108.

ICEIS-v2-2003-BendouM #learning #network #semistructured data

Learning Bayesian Networks From Noisy Data (MB, PM), pp. 26–33.

ECIR-2003-AunimoHKMPV #semistructured data

Question Answering System for Incomplete and Noisy Data (LA, OH, RK, JM, RP, OV), pp. 193–206.

ICML-2003-GeibelW #learning

Perceptron Based Learning with Example Dependent and Noisy Costs (PG, FW), pp. 218–225.

ICML-2003-Graepel #difference #equation #linear #process

Solving Noisy Linear Operator Equations by Gaussian Processes: Application to Ordinary and Partial Differential Equations (TG), pp. 234–241.

ICML-2003-SebbanJ #approach #grammar inference #on the #semistructured data #statistics

On State Merging in Grammatical Inference: A Statistical Approach for Dealing with Noisy Data (MS, JCJ), pp. 688–695.

KDD-2003-YiLL #data mining #mining #web

Eliminating noisy information in Web pages for data mining (LY, BL, XL), pp. 296–305.

SIGMOD-2002-YangWYH #mining

Mining long sequential patterns in a noisy environment (JY, WW, PSY, JH), pp. 406–417.

STOC-2002-AroraK #algebra #semistructured data

Fitting algebraic curves to noisy data (SA, SK), pp. 162–169.

ICPR-v4-2002-IbrahimovSD #analysis #clustering #performance #similarity #topic

The Performance Analysis of a Chi-square Similarity Measure for Topic Related Clustering of Noisy Transcripts (OI, IKS, ND), pp. 285–288.

SAC-2002-CarlssonJ #difference #game studies

Differences between the iterated prisoner’s dilemma and the chicken game under noisy conditions (BC, KIJ), pp. 42–48.

CIKM-2001-Collins-ThompsonSD #string

Improved String Matching Under Noisy Channel Conditions (KCT, CS, STD), pp. 357–364.

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-2000-AndersonMC #approach #optimisation #parametricity

A Nonparametric Approach to Noisy and Costly Optimization (BSA, AWM, DC), pp. 17–24.

ICML-2000-Eskin #detection #probability #semistructured data #using

Anomaly Detection over Noisy Data using Learned Probability Distributions (EE), pp. 255–262.

ICML-2000-TowellPM #learning

Learning Priorities From Noisy Examples (GGT, TP, MRM), pp. 1031–1038.

ICML-2000-ZupanBBD #concept #induction #semistructured data

Induction of Concept Hierarchies from Noisy Data (BZ, IB, MB, JD), pp. 1199–1206.

ICPR-v1-2000-Brauer-BurchardtV #image #robust

Robust Vanishing Point Determination in Noisy Images (CBB, KV), pp. 1559–1562.

ICPR-v3-2000-CohenD #estimation #image

Motion Estimation in Noisy Ultrasound Images by Maximum Likelihood (BC, ID), pp. 3186–3189.

ICPR-v3-2000-DekeyserBPP #2d #image #parametricity #sequence

Super-Resolution from Noisy Image Sequences Exploiting a 2D Parametric Motion Model (FD, PB, PP, ÉP), pp. 3354–3357.

ICPR-v4-2000-ChouB #image #locality

Accurate Localization of Edges in Noisy Volume Images (PcC, MB), pp. 4760–4763.

ICPR-v4-2000-Lopez-de-TeruelRG #algorithm #image #parallel

A Parallel Algorithm for Tracking of Segments in Noisy Edge Images (PELdT, AR, JMG), pp. 4807–4811.

ICDAR-1999-NegishiKHW #automation

Character Extraction from Noisy Background for an Automatic Reference System (HN, JK, HH, TW), pp. 143–146.

ICML-1999-Teng #semistructured data

Correcting Noisy Data (CMT), pp. 239–248.

ICML-1998-MooreSBL #learning #named #optimisation

Q2: Memory-Based Active Learning for Optimizing Noisy Continuous Functions (AWM, JGS, JAB, MSL), pp. 386–394.

ICSE-1998-TesorieroZ #re-engineering

A Model of Noisy Software Engineering Data (Status Report) (RT, MVZ), pp. 461–464.

ICDAR-1997-CesariniFGMSS #architecture #recognition

A Neural-Based Architecture for Spot-Noisy Logo Recognition (FC, EF, MG, SM, JS, GS), pp. 175–179.

HCI-CC-1997-Vanderheiden97a #architecture #interface

Use of a Common Table Architecture for Creating Hands Free, Eyes Free, Noisy Environment (Flex-Modal, Flex-Input) Interfaces (GCV), pp. 449–452.

STOC-1996-EvansP #bound

Lower Bounds for Noisy Boolean Decision Trees (WSE, NP), pp. 620–628.

ICML-1996-OkamotoY #analysis #classification #nearest neighbour

Theoretical Analysis of the Nearest Neighbor Classifier in Noisy Domains (SO, NY), pp. 355–363.

ICPR-1996-AkhmetshinL #difference #fourier #image #principle #re-engineering

The reconstruction of signals and images from the noisy Fourier transform phase by means of the generalized difference principle (AMA, IL), pp. 370–375.

ICPR-1996-ChenY #symmetry #using #visual notation

Thinning noisy binary patterns using human visual symmetry (YSC, YTY), pp. 146–150.

ICPR-1996-NetanyahuPRS #detection #image #robust

Robust detection of road segments in noisy aerial images (NSN, VP, AR, AJS), pp. 151–155.

KDD-1996-Czyzewski #mining

Mining Knowledge in Noisy Audio Data (AC), pp. 220–225.

KDD-1996-Rymon

SE-Trees Outperform Decision Trees in Noisy Domains (RR), pp. 331–334.

ML-1991-DzeroskiL #comparison #empirical #learning

Learning Relations from Noisy Examples: An Empirical Comparison of LINUS and FOIL (SD, NL), pp. 399–402.

ML-1991-PachowiczB #concept #effectiveness #recognition

Improving Recognition Effectiveness of Noisy Texture Concepts (PP, JWB), pp. 625–629.

ML-1988-TanE #classification #network #using

Using Weighted Networks to Represent Classification Knowledge in Noisy Domains (MT, LJE), pp. 121–134.