Stem unlabel$ (all stems)

47 papers:

CASE-2015-ZhuCS #energy #linear #process #recognition #using

Using unlabeled acoustic data with locality-constrained linear coding for energy-related activity recognition in buildings (QZ, ZC, YCS), pp. 174–179.

ICML-2015-PlessisNS #learning

Convex Formulation for Learning from Positive and Unlabeled Data (MCdP, GN, MS), pp. 1386–1394.

KDD-2015-LanH #complexity #learning #multi

Reducing the Unlabeled Sample Complexity of Semi-Supervised Multi-View Learning (CL, JH), pp. 627–634.

SIGIR-2015-Bravo-MarquezFP #twitter #word

From Unlabelled Tweets to Twitter-specific Opinion Words (FBM, EF, BP), pp. 743–746.

ICML-c3-2013-AlmingolML #behaviour #learning #multi

Learning Multiple Behaviors from Unlabeled Demonstrations in a Latent Controller Space (JA, LM, ML), pp. 136–144.

ICML-c3-2013-BalcanBM #learning #ontology

Exploiting Ontology Structures and Unlabeled Data for Learning (NB, AB, YM), pp. 1112–1120.

KDD-2013-FeiKSNMH #detection #learning

Heat pump detection from coarse grained smart meter data with positive and unlabeled learning (HF, YK, SS, MRN, SKM, JH), pp. 1330–1338.

CIKM-2012-FukumotoYMS #classification #documentation

Text classification with relatively small positive documents and unlabeled data (FF, TY, SM, YS), pp. 2315–2318.

ICML-2012-ChambersJ #learning

Learning the Central Events and Participants in Unlabeled Text (NC, DJ), p. 3.

ICPR-2012-KongW12a #clustering

Transfer heterogeneous unlabeled data for unsupervised clustering (SK, DW), pp. 1193–1196.

ICPR-2012-KunchevaF #detection #feature model #multi #streaming

PCA feature extraction for change detection in multidimensional unlabelled streaming data (LIK, WJF), pp. 1140–1143.

ICPR-2012-TakedaTRKKYTOMT #image #recognition #self

Self-training with unlabeled regions for NBI image recognition (TT, TT, BR, KK, TK, SY, YT, KO, RM, ST), pp. 25–28.

ICPR-2012-XueCH #classification #constraints #kernel

Discriminative indefinite kernel classifier from pairwise constraints and unlabeled data (HX, SC, JH), pp. 497–500.

CIKM-2011-SellamanickamGS #approach #learning #ranking

A pairwise ranking based approach to learning with positive and unlabeled examples (SS, PG, SKS), pp. 663–672.

ICML-2011-LiZ #towards

Towards Making Unlabeled Data Never Hurt (YFL, ZHZ), pp. 1081–1088.

ICML-2011-UrnerSB #predict

Access to Unlabeled Data can Speed up Prediction Time (RU, SSS, SBD), pp. 641–648.

RTA-2011-SternagelT #composition #semantics

Modular and Certified Semantic Labeling and Unlabeling (CS, RT), pp. 329–344.

CIKM-2010-ChaturvediFSM #classification #scalability

Estimating accuracy for text classification tasks on large unlabeled data (SC, TAF, LVS, MKM), pp. 889–898.

CIKM-2010-QianCXQ #how

How about utilizing ordinal information from the distribution of unlabeled data (MQ, BC, HX, HQ), pp. 289–298.

ICPR-2010-BaghshahS #constraints #kernel #learning #performance

Efficient Kernel Learning from Constraints and Unlabeled Data (MSB, SBS), pp. 3364–3367.

SIGIR-2010-ArguelloDP

Vertical selection in the presence of unlabeled verticals (JA, FD, JFP), pp. 691–698.

CIKM-2009-QianNZ #multi #performance

Efficient multi-class unlabeled constrained semi-supervised SVM (MQ, FN, CZ), pp. 1665–1668.

CIKM-2009-QiCKKW #learning

Combining labeled and unlabeled data with word-class distribution learning (YQ, RC, PPK, KK, JW), pp. 1737–1740.

ICPR-2008-KarlssonA #image

MDL patch correspondences on unlabeled images (JK, KÅ), pp. 1–5.

KDD-2008-BifetG #adaptation #data type #mining

Mining adaptively frequent closed unlabeled rooted trees in data streams (AB, RG), pp. 34–42.

KDD-2008-ElkanN #classification #learning

Learning classifiers from only positive and unlabeled data (CE, KN), pp. 213–220.

SAC-2008-TanWWC #detection #problem #semantics #using

Using unlabeled data to handle domain-transfer problem of semantic detection (ST, YW, GW, XC), pp. 896–903.

ICML-2007-RainaBLPN #learning #self

Self-taught learning: transfer learning from unlabeled data (RR, AB, HL, BP, AYN), pp. 759–766.

ICML-2005-ZhouHS #graph #learning

Learning from labeled and unlabeled data on a directed graph (DZ, JH, BS), pp. 1036–1043.

SIGIR-2005-BeitzelJFGLCK #automation #classification #query #using #web

Automatic web query classification using labeled and unlabeled training data (SMB, ECJ, OF, DAG, DDL, AC, AK), pp. 581–582.

ICPR-v3-2004-WuZZ #feature model #linear #using

Relevant Linear Feature Extraction Using Side-information and Unlabeled Data (FW, YZ, CZ), pp. 582–585.

SIGIR-2004-FanL #classification #semantics #video

Semantic video classification by integrating unlabeled samples for classifier training (JF, HL), pp. 592–593.

CIKM-2003-YuZH #classification #documentation

Text classification from positive and unlabeled documents (HY, CZ, JH), pp. 232–239.

ICML-2003-LeeL #learning #using

Learning with Positive and Unlabeled Examples Using Weighted Logistic Regression (WSL, BL), pp. 448–455.

ICML-2002-Ghani #categorisation #multi

Combining Labeled and Unlabeled Data for MultiClass Text Categorization (RG), pp. 187–194.

ICML-2002-Mladenic #learning #normalisation #using #word

Learning word normalization using word suffix and context from unlabeled data (DM), pp. 427–434.

ICML-2002-RaskuttiFK #classification #clustering #parametricity #using

Using Unlabelled Data for Text Classification through Addition of Cluster Parameters (BR, HLF, AK), pp. 514–521.

KDD-2002-BennettDM

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

KDD-2002-RaskuttiFK #classification #clustering #using

Combining clustering and co-training to enhance text classification using unlabelled data (BR, HLF, AK), pp. 620–625.

SIGIR-2002-AminiG #learning #summary

The use of unlabeled data to improve supervised learning for text summarization (MRA, PG), pp. 105–112.

SIGIR-2002-Boyapati #classification #using

Improving hierarchical text classification using unlabeled data (VB), pp. 363–364.

ICML-2001-BlumC #graph #learning #using

Learning from Labeled and Unlabeled Data using Graph Mincuts (AB, SC), pp. 19–26.

KDD-2001-YamanishiT

Discovering outlier filtering rules from unlabeled data: combining a supervised learner with an unsupervised learner (KY, JiT), pp. 389–394.

ICML-2000-GoldmanZ #learning

Enhancing Supervised Learning with Unlabeled Data (SAG, YZ), pp. 327–334.

ICML-2000-ZelikovitzH #classification #problem #using

Improving Short-Text Classification using Unlabeled Data for Classification Problems (SZ, HH), pp. 1191–1198.

ICPR-v1-2000-NelsonS #3d #empirical #learning #modelling #recognition

Learning 3D Recognition Models for General Objects from Unlabeled Imagery: An Experiment in Intelligent Brute Force (RCN, AS), pp. 1001–1008.

ICPR-v1-2000-WuTH #feedback #image #retrieval

Integrating Unlabeled Images for Image Retrieval Based on Relevance Feedback (YW, QT, TSH), pp. 1021–1024.