Stem mixtur$ (all stems)

200 papers:

PLDI-2015-EmaniO #approach #runtime

Celebrating diversity: a mixture of experts approach for runtime mapping in dynamic environments (MKE, MFPO), pp. 499–508.

STOC-2015-GeHK #learning

Learning Mixtures of Gaussians in High Dimensions (RG, QH, SMK), pp. 761–770.

STOC-2015-HardtP #bound #learning

Tight Bounds for Learning a Mixture of Two Gaussians (MH, EP), pp. 753–760.

STOC-2015-LiRSS #learning #statistics

Learning Arbitrary Statistical Mixtures of Discrete Distributions (JL, YR, LJS, CS), pp. 743–752.

ECIR-2015-KimVBR #clustering #multi

Temporal Multinomial Mixture for Instance-Oriented Evolutionary Clustering (YMK, JV, SB, MAR), pp. 593–604.

ICML-2015-GeCWG #distributed #modelling #process

Distributed Inference for Dirichlet Process Mixture Models (HG, YC, MW, ZG), pp. 2276–2284.

ICML-2015-YenLZRD #approach #modelling #process

A Convex Exemplar-based Approach to MAD-Bayes Dirichlet Process Mixture Models (IEHY, XL, KZ, PKR, ISD), pp. 2418–2426.

SIGIR-2015-WangSWZSLL #cumulative #recommendation

An Entity Class-Dependent Discriminative Mixture Model for Cumulative Citation Recommendation (JW, DS, QW, ZZ, LS, LL, CYL), pp. 635–644.

DAC-2014-RoyKCBC #streaming #using

Demand-Driven Mixture Preparation and Droplet Streaming using Digital Microfluidic Biochips (SR, SK, PPC, BBB, KC), p. 6.

CIKM-2014-HongBH #classification #framework #multi

A Mixtures-of-Trees Framework for Multi-Label Classification (CH, IB, MH), pp. 211–220.

ICML-c2-2014-SunIM #classification #learning #linear

Learning Mixtures of Linear Classifiers (YS, SI, AM), pp. 721–729.

ICML-c2-2014-ToshD #bound

Lower Bounds for the Gibbs Sampler over Mixtures of Gaussians (CT, SD), pp. 1467–1475.

ICPR-2014-ChamroukhiBG #clustering #parametricity

Bayesian Non-parametric Parsimonious Gaussian Mixture for Clustering (FC, MB, HG), pp. 1460–1465.

ICPR-2014-HasnatAT #clustering #image #using

Unsupervised Clustering of Depth Images Using Watson Mixture Model (MAH, OA, AT), pp. 214–219.

ICPR-2014-IoannidisCL #clustering #modelling #multi #using

Key-Frame Extraction Using Weighted Multi-view Convex Mixture Models and Spectral Clustering (AI, VC, AL), pp. 3463–3468.

ICPR-2014-TsuchiyaMT #network

Exemplar Network: A Generalized Mixture Model (CT, TM, AT), pp. 598–603.

KDD-2014-LichmanS #kernel #modelling

Modeling human location data with mixtures of kernel densities (ML, PS), pp. 35–44.

KDD-2014-YinW #approach #clustering #modelling #multi

A dirichlet multinomial mixture model-based approach for short text clustering (JY, JW), pp. 233–242.

KDIR-2014-BigdeliMRM #clustering #summary

Arbitrary Shape Cluster Summarization with Gaussian Mixture Model (EB, MM, BR, SM), pp. 43–52.

RecSys-2014-KimC #collaboration #predict

Bayesian binomial mixture model for collaborative prediction with non-random missing data (YDK, SC), pp. 201–208.

ICML-c1-2013-WilliamsonDX #markov #modelling #monte carlo #parallel #parametricity

Parallel Markov Chain Monte Carlo for Nonparametric Mixture Models (SW, AD, EPX), pp. 98–106.

ICML-c2-2013-YangZ #process

Mixture of Mutually Exciting Processes for Viral Diffusion (SHY, HZ), pp. 1–9.

ICML-c3-2013-ChagantyL #linear

Spectral Experts for Estimating Mixtures of Linear Regressions (ATC, PL), pp. 1040–1048.

ICML-c3-2013-RossD #constraints #parametricity #process

Nonparametric Mixture of Gaussian Processes with Constraints (JCR, JGD), pp. 1346–1354.

ICML-c3-2013-YoshiiTMG #infinity

Infinite Positive Semidefinite Tensor Factorization for Source Separation of Mixture Signals (KY, RT, DM, MG), pp. 576–584.

CASE-2012-NganYY #modelling #process

Modeling of traffic data characteristics by Dirichlet Process Mixtures (HYTN, NHCY, AGOY), pp. 224–229.

ICML-2012-KoS #modelling #scalability

Large Scale Variational Bayesian Inference for Structured Scale Mixture Models (YJK, MWS), p. 229.

ICML-2012-NaimG #algorithm #convergence

Convergence of the EM Algorithm for Gaussian Mixtures with Unbalanced Mixing Coefficients (IN, DG), p. 185.

ICML-2012-ReyR #clustering

Copula Mixture Model for Dependency-seeking Clustering (MR, VR), p. 40.

ICML-2012-TangSH

Deep Mixtures of Factor Analysers (YT, RS, GEH), p. 147.

ICPR-2012-BoulmerkaA #segmentation #using

Thresholding-based segmentation revisited using mixtures of generalized Gaussian distributions (AB, MSA), pp. 2894–2897.

ICPR-2012-FrouzeshPH #modelling #optimisation

A combined method for finding best starting points for optimisation in bernoulli mixture models (FF, SP, YH), pp. 1128–1131.

ICPR-2012-HeLL #image #using

Single image super-resolution using Gaussian Mixture Model (HH, JL, XL), pp. 1916–1919.

ICPR-2012-KhanER #detection #image

A Gamma-Gaussian mixture model for detection of mitotic cells in breast cancer histopathology images (AMK, HED, NMR), pp. 149–152.

ICPR-2012-LiuBFF

Complex Gaussian Mixture Model for fingerprint minutiae (CL, JB, XF, JF), pp. 545–548.

ICPR-2012-Nielsen #statistics

Closed-form information-theoretic divergences for statistical mixtures (FN), pp. 1723–1726.

ICPR-2012-SchwanderSNB

k-MLE for mixtures of generalized Gaussians (OS, AJS, FN, YB), pp. 2825–2828.

ICPR-2012-TsuboshitaKFO #adaptation #image #using

Image annotation using adapted Gaussian mixture model (YT, NK, MF, MO), pp. 1346–1350.

ICPR-2012-ZhangWN #detection #feature model #student

Bayesian feature selection and model detection for student’s t-mixture distributions (HZ, QMJW, TMN), pp. 1631–1634.

KDD-2012-GunnemannFS #clustering #modelling #multi #using

Multi-view clustering using mixture models in subspace projections (SG, IF, TS), pp. 132–140.

SIGIR-2012-HongS #algorithm #multi #retrieval

Mixture model with multiple centralized retrieval algorithms for result merging in federated search (DH, LS), pp. 821–830.

ICDAR-2011-GimenezAJS #modelling #recognition

Discriminative Bernoulli Mixture Models for Handwritten Digit Recognition (AG, JAF, AJ, NS), pp. 558–562.

VLDB-2012-RanuS11 #query

Answering Top-k Queries Over a Mixture of Attractive and Repulsive Dimensions (SR, AKS), pp. 169–180.

CIKM-2011-KimO #dependence #process

Accounting for data dependencies within a hierarchical dirichlet process mixture model (DK, AHO), pp. 873–878.

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.

SAC-2011-HeinenE #incremental #modelling #using

Incremental feature-based mapping from sonar data using Gaussian mixture models (MRH, PME), pp. 1370–1375.

CASE-2010-Tobon-MejiaMZT #markov

A mixture of Gaussians Hidden Markov Model for failure diagnostic and prognostic (DATM, KM, NZ, GT), pp. 338–343.

DAC-2010-DhimanMR #modelling #online #predict #using

A system for online power prediction in virtualized environments using Gaussian mixture models (GD, KM, TR), pp. 807–812.

STOC-2010-KalaiMV #learning

Efficiently learning mixtures of two Gaussians (ATK, AM, GV), pp. 553–562.

CIKM-2010-ChiHY

Mixture model label propagation (MC, XH, SY), pp. 1889–1892.

ICML-2010-BardenetK #algorithm #optimisation

Surrogating the surrogate: accelerating Gaussian-process-based global optimization with a mixture cross-entropy algorithm (RB, BK), pp. 55–62.

ICPR-2010-Allili #retrieval #using

Wavelet-Based Texture Retrieval Using a Mixture of Generalized Gaussian Distributions (MSA), pp. 3143–3146.

ICPR-2010-AriA #estimation #modelling #optimisation #using

Maximum Likelihood Estimation of Gaussian Mixture Models Using Particle Swarm Optimization (CA, SA), pp. 746–749.

ICPR-2010-BruneauGP #parametricity #probability

Aggregation of Probabilistic PCA Mixtures with a Variational-Bayes Technique Over Parameters (PB, MG, FP), pp. 702–705.

ICPR-2010-IsmailF #clustering #finite #modelling #robust

Possibilistic Clustering Based on Robust Modeling of Finite Generalized Dirichlet Mixture (MMBI, HF), pp. 573–576.

ICPR-2010-JiLZ #clustering #modelling

CDP Mixture Models for Data Clustering (YJ, TL, HZ), pp. 637–640.

ICPR-2010-Martinez-UsoPS #image #segmentation

A Semi-supervised Gaussian Mixture Model for Image Segmentation (AMU, FP, JMS), pp. 2941–2944.

ICPR-2010-MemonLM #modelling #verification

Information Theoretic Expectation Maximization Based Gaussian Mixture Modeling for Speaker Verification (SM, ML, NCM), pp. 4536–4540.

ICPR-2010-NacereddineTZH10a #algorithm #image #modelling #segmentation #symmetry

Asymmetric Generalized Gaussian Mixture Models and EM Algorithm for Image Segmentation (NN, ST, DZ, LH), pp. 4557–4560.

ICPR-2010-NielsenBS #clustering

Bhattacharyya Clustering with Applications to Mixture Simplifications (FN, SB, OS), pp. 1437–1440.

ICPR-2010-SlimaneKAIH #modelling #recognition

Gaussian Mixture Models for Arabic Font Recognition (FS, SK, AMA, RI, JH), pp. 2174–2177.

ICPR-2010-StadelmannF #recognition

Dimension-Decoupled Gaussian Mixture Model for Short Utterance Speaker Recognition (TS, BF), pp. 1602–1605.

ICPR-2010-UlkerGC #modelling #process

Annealed SMC Samplers for Dirichlet Process Mixture Models (YÜ, BG, ATC), pp. 2808–2811.

ICPR-2010-YukselG #classification #detection

Variational Mixture of Experts for Classification with Applications to Landmine Detection (SEY, PDG), pp. 2981–2984.

KDD-2010-SomaiyaJR #learning #modelling

Mixture models for learning low-dimensional roles in high-dimensional data (MS, CMJ, SR), pp. 909–918.

KDD-2010-YuHW #clustering #documentation #feature model #process

Document clustering via dirichlet process mixture model with feature selection (GY, RzH, ZW), pp. 763–772.

DAC-2009-TakahashiYT #analysis #statistics

A Gaussian mixture model for statistical timing analysis (ST, YY, ST), pp. 110–115.

ICDAR-2009-ChenLJ #estimation #modelling #orthogonal #recognition

Unsupervised Selection and Discriminative Estimation of Orthogonal Gaussian Mixture Models for Handwritten Digit Recognition (XC, XL, YJ), pp. 1151–1155.

ICDAR-2009-GimenezJ #embedded #recognition #word

Embedded Bernoulli Mixture HMMs for Handwritten Word Recognition (AG, AJ), pp. 896–900.

ICSM-2009-LiuPFGC #modelling #topic

Modeling class cohesion as mixtures of latent topics (YL, DP, RF, TG, NC), pp. 233–242.

ECIR-2009-StathopoulosJ #automation #image

Bayesian Mixture Hierarchies for Automatic Image Annotation (VS, JMJ), pp. 138–149.

ICML-2009-VlassisT #learning

Model-free reinforcement learning as mixture learning (NV, MT), pp. 1081–1088.

KDIR-2009-FujimotoHM #modelling #visualisation

Item-user Preference Mapping with Mixture Models — Data Visualization for Item Preference (YF, HH, NM), pp. 105–111.

MLDM-2009-HasanG #adaptation #classification #modelling

Sequential EM for Unsupervised Adaptive Gaussian Mixture Model Based Classifier (BASH, JQG), pp. 96–106.

SIGIR-2009-ZhangHS #approximate #modelling

Approximating true relevance distribution from a mixture model based on irrelevance data (PZ, YH, DS), pp. 107–114.

SAC-2009-BaechlerBH #image #modelling #using #verification

Labeled images verification using Gaussian mixture models (MB, JLB, JH), pp. 1331–1335.

ECIR-2008-SerdyukovH #documentation #modelling

Modeling Documents as Mixtures of Persons for Expert Finding (PS, DH), pp. 309–320.

ICML-2008-ShiBY #learning #modelling #using

Data spectroscopy: learning mixture models using eigenspaces of convolution operators (TS, MB, BY), pp. 936–943.

ICML-2008-WangYZ #adaptation #kernel #learning #multi

Adaptive p-posterior mixture-model kernels for multiple instance learning (HYW, QY, HZ), pp. 1136–1143.

ICML-2008-ZhangDT #algorithm

Estimating local optimums in EM algorithm over Gaussian mixture model (ZZ, BTD, AKHT), pp. 1240–1247.

ICPR-2008-BruneauGP #approach #modelling #reduction

Parameter-based reduction of Gaussian mixture models with a variational-Bayes approach (PB, MG, FP), pp. 1–4.

ICPR-2008-DingB08a #difference #probability #recognition

Probabilistic mixtures of differential profiles for shape recognition (LD, MB), pp. 1–4.

ICPR-2008-HaqueMP #adaptation #detection #generative #multi #robust

Improved Gaussian mixtures for robust object detection by adaptive multi-background generation (MH, MMM, MP), pp. 1–4.

ICPR-2008-HortaMF #algorithm #clustering #comparison #image #using

A comparison of clustering fully polarimetric SAR images using SEM algorithm and G0P mixture modelwith different initializations (MMH, NDAM, ACF), pp. 1–4.

ICPR-2008-LiDM #feature model #learning #locality #using

Localized feature selection for Gaussian mixtures using variational learning (YL, MD, YM), pp. 1–4.

ICPR-2008-PiccardiGO #classification #modelling #reduction

Maximum-likelihood dimensionality reduction in gaussian mixture models with an application to object classification (MP, HG, AFO), pp. 1–4.

ICPR-2008-WangWCW #algorithm #clustering #learning

A clustering algorithm combine the FCM algorithm with supervised learning normal mixture model (WW, CW, XC, AW), pp. 1–4.

KDD-2008-SongJRG #linear

A bayesian mixture model with linear regression mixing proportions (XS, CJ, SR, JG), pp. 659–667.

HCI-MIE-2007-JungKK #estimation #image #modelling #using

Human Pose Estimation Using a Mixture of Gaussians Based Image Modeling (DJJ, KSK, HJK), pp. 649–658.

ICML-2007-KimP #learning #recursion

A recursive method for discriminative mixture learning (MK, VP), pp. 409–416.

ICML-2007-KirshnerS #infinity

Infinite mixtures of trees (SK, PS), pp. 417–423.

ICML-2007-LiangJT #modelling

A permutation-augmented sampler for DP mixture models (PL, MIJ, BT), pp. 545–552.

ICML-2007-LiaoLC #classification #semistructured data

Quadratically gated mixture of experts for incomplete data classification (XL, HL, LC), pp. 553–560.

ICML-2007-MimnoLM #topic

Mixtures of hierarchical topics with Pachinko allocation (DMM, WL, AM), pp. 633–640.

ICML-2007-SunKR #fault #metric #robust

Robust mixtures in the presence of measurement errors (JS, AK, SR), pp. 847–854.

KDD-2007-Sandler #analysis #modelling #probability

Hierarchical mixture models: a probabilistic analysis (MS), pp. 580–589.

KDD-2007-SatoN #documentation #information management #multi #parametricity #topic #using

Knowledge discovery of multiple-topic document using parametric mixture model with dirichlet prior (IS, HN), pp. 590–598.

SIGIR-2007-ZhangX #estimation #modelling #performance

Fast exact maximum likelihood estimation for mixture of language models (YZ, WX), pp. 865–866.

DAC-2006-KanjJN #analysis #design

Mixture importance sampling and its application to the analysis of SRAM designs in the presence of rare failure events (RK, RVJ, SRN), pp. 69–72.

ICML-2006-GeJ #approximate #consistency #multi

A note on mixtures of experts for multiclass responses: approximation rate and Consistent Bayesian Inference (YG, WJ), pp. 329–335.

ICML-2006-LiM #correlation #modelling #topic

Pachinko allocation: DAG-structured mixture models of topic correlations (WL, AM), pp. 577–584.

ICML-2006-SrebroSR #clustering

An investigation of computational and informational limits in Gaussian mixture clustering (NS, GS, STR), pp. 865–872.

ICML-2006-XingSJT #multi #process #type inference

Bayesian multi-population haplotype inference via a hierarchical dirichlet process mixture (EPX, KAS, MIJ, YWT), pp. 1049–1056.

ICPR-v1-2006-LiL #probability

Probabilistic Image-Based Rendering with Gaussian Mixture Model (WL, BL), pp. 179–182.

ICPR-v1-2006-LinWZZ #optimisation

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

ICPR-v2-2006-BenaventRS #algorithm #modelling #named

EBEM: An Entropy-based EM Algorithm for Gaussian Mixture Models (APB, FER, JMS), pp. 451–455.

ICPR-v2-2006-ChenY #modelling #using #video

Exploiting High Dimensional Video Features Using Layered Gaussian Mixture Models (DC, JY), pp. 1078–1081.

ICPR-v2-2006-GrimHSP #approach #modelling #using

A Subspace Approach to Texture Modelling by Using Gaussian Mixtures (JG, MH, PS, PP), pp. 235–238.

ICPR-v2-2006-GuoQ #3d #learning

Learning and Inference of 3D Human Poses from Gaussian Mixture Modeled Silhouettes (FG, GQ), pp. 43–47.

ICPR-v2-2006-IlonenPKK #classification

Gaussian mixture pdf in one-class classification: computing and utilizing confidence values (JI, PP, JKK, HK), pp. 577–580.

ICPR-v2-2006-Lu #evaluation #similarity

Joint Distributions based on DFB and Gaussian Mixtures for Evaluation of Style Similarity among Paintings (XL), pp. 865–868.

ICPR-v2-2006-SridharanBG

Competitive Mixtures of Simple Neurons (KS, MJB, VG), pp. 494–497.

ICPR-v3-2006-AlahariPJ #learning #online #recognition

Learning Mixtures of Offline and Online features for Handwritten Stroke Recognition (KA, SLP, CVJ), pp. 379–382.

ICPR-v3-2006-ScheundersB #component #image #multi #using

Wavelet denoising of multicomponent images, using a Gaussian Scale Mixture model (PS, SDB), pp. 754–757.

ICPR-v3-2006-SchlapbachB #identification #modelling #using

Off-lineWriter Identification Using Gaussian Mixture Models (AS, HB), pp. 992–995.

ICPR-v3-2006-TanakaO

Neighbor Pixel Mixture (MT, MO), pp. 647–650.

ICPR-v4-2006-KrugerSKAW #recognition #speech

Mixture of Support Vector Machines for HMM based Speech Recognition (SEK, MS, MK, EA, AW), pp. 326–329.

ICPR-v4-2006-LeilaC #performance #recognition #speech

Efficient Gaussian Mixture for Speech Recognition (LZ, GC), pp. 294–297.

KDD-2006-MeiZ #mining

A mixture model for contextual text mining (QM, CZ), pp. 649–655.

SIGIR-2006-TaoZ #estimation #feedback #modelling #pseudo #robust

Regularized estimation of mixture models for robust pseudo-relevance feedback (TT, CZ), pp. 162–169.

SAC-2006-BelsisFGS #classification

SF-HME system: a hierarchical mixtures-of-experts classification system for spam filtering (PB, KF, SG, CS), pp. 354–360.

SAC-2006-RouguiRAGM #documentation #modelling #retrieval #scalability #set

Hierarchical organization of a set of Gaussian mixture speaker models for scaling up indexing and retrieval in audio documents (JER, MR, DA, MG, JMM), pp. 1369–1373.

ICML-2005-CarneyCDL #network #predict #probability #using

Predicting probability distributions for surf height using an ensemble of mixture density networks (MC, PC, JD, CL), pp. 113–120.

ICML-2005-SajamaO05a #modelling #reduction #using

Supervised dimensionality reduction using mixture models (S, AO), pp. 768–775.

ICML-2005-ZhuL #graph #induction #learning #modelling #scalability

Harmonic mixtures: combining mixture models and graph-based methods for inductive and scalable semi-supervised learning (XZ, JDL), pp. 1052–1059.

MLDM-2005-BouguilaZ #approach #estimation #finite

MML-Based Approach for Finite Dirichlet Mixture Estimation and Selection (NB, DZ), pp. 42–51.

MLDM-2005-XiaWZL #data mining #mining #modelling #random

Mixture Random Effect Model Based Meta-analysis for Medical Data Mining (YX, SW, CZ, SL), pp. 630–640.

MLDM-2005-ZhangZ #component #finite #modelling

Finite Mixture Models with Negative Components (BZ, CZ), pp. 31–41.

SAC-2005-HanXZG #ambiguity #naive bayes

A hierarchical naive Bayes mixture model for name disambiguation in author citations (HH, WX, HZ, CLG), pp. 1065–1069.

STOC-2004-KleinbergS #collaboration #modelling #using

Using mixture models for collaborative filtering (JMK, MS), pp. 569–578.

CIKM-2004-SiJ #collaboration #exponential

Unified filtering by combining collaborative filtering and content-based filtering via mixture model and exponential model (LS, RJ), pp. 156–157.

ICML-2004-BanerjeeDGM #analysis #estimation #exponential #product line

An information theoretic analysis of maximum likelihood mixture estimation for exponential families (AB, ISD, JG, SM).

ICML-2004-SuD #automation #component #probability

Automated hierarchical mixtures of probabilistic principal component analyzers (TS, JGD).

ICPR-v1-2004-BouguilaZ #finite #learning #modelling

A Powreful Finite Mixture Model Based on the Generalized Dirichlet Distribution: Unsupervised Learning and Applications (NB, DZ), pp. 280–283.

ICPR-v1-2004-SalahA #incremental

Incremental Mixtures of Factor Analysers (AAS, EA), pp. 276–279.

ICPR-v1-2004-Wakahara #adaptation #correlation #modelling #normalisation #using

Adaptive Normalization of Handwritten Characters Using GAT Correlation and Mixture Models (TW), pp. 393–396.

ICPR-v2-2004-DowsonB #metric

Metric Mixtures for Mutual Information (M^3 I) Tracking (NDHD, RB), pp. 752–756.

ICPR-v2-2004-MaD #adaptation #classification #using #word

Adaptive Word Style Classification Using a Gaussian Mixture Model (HM, DSD), pp. 606–609.

ICPR-v2-2004-NockP #bias #estimation

Grouping with Bias for Distribution-Free Mixture Model Estimation (RN, VP), pp. 44–47.

ICPR-v2-2004-VargaB04a #recognition #using

Off-line Handwritten Textline Recognition Using a Mixture of Natural and Synthetic Training Data (TV, HB), pp. 545–549.

ICPR-v2-2004-ZiouB #analysis #finite #image #learning #using

Unsupervised Learning of a Finite Gamma Mixture Using MML: Application to SAR Image Analysis (DZ, NB), pp. 68–71.

ICPR-v2-2004-Zivkovic #adaptation

Improved Adaptive Gaussian Mixture Model for Background Subtraction (ZZ), pp. 28–31.

ICPR-v3-2004-HaindlGSPK

A Gaussian Mixture-Based Colour Texture Model (MH, JG, PS, PP, MK), pp. 177–180.

ICPR-v3-2004-JuanV #image #modelling

Bernoulli Mixture Models for Binary Images (AJ, EV), pp. 367–370.

ICPR-v3-2004-MoW #modelling #segmentation #using #video

Video Modelling and Segmentation Using Gaussian Mixture Models (XM, RW), pp. 854–857.

ICPR-v4-2004-Cheung #algorithm #automation #clustering #towards

A Rival Penalized EM Algorithm towards Maximizing Weighted Likelihood for Density Mixture Clustering with Automatic Model Selection (YmC), pp. 633–636.

ICPR-v4-2004-FuYHT #clustering #detection #multi #using

Mixture Clustering Using Multidimensional Histograms for Skin Detection (ZF, JY, WH, TT), pp. 549–552.

ICPR-v4-2004-WangT #modelling #recognition

Bayesian Face Recognition Based on Gaussian Mixture Models (XW, XT), pp. 142–145.

KDD-2004-BiZB #kernel

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

KDD-2004-MorinagaY #finite #roadmap #topic #using

Tracking dynamics of topic trends using a finite mixture model (SM, KY), pp. 811–816.

KDD-2004-PavlovBDKP #classification #clustering #documentation #multi #naive bayes #preprocessor

Document preprocessing for naive Bayes classification and clustering with mixture of multinomials (DP, RB, BD, SK, JP), pp. 829–834.

KDD-2004-ZhaiVY #comparative #mining

A cross-collection mixture model for comparative text mining (CZ, AV, BY), pp. 743–748.

SIGIR-2004-TaoZ #feedback #pseudo

A two-stage mixture model for pseudo feedback (TT, CZ), pp. 486–487.

ICML-2003-CozmanCC #learning #modelling

Semi-Supervised Learning of Mixture Models (FGC, IC, MCC), pp. 99–106.

ICML-2003-KlautauJO #classification #comparison #kernel #modelling

Discriminative Gaussian Mixture Models: A Comparison with Kernel Classifiers (AK, NJ, AO), pp. 353–360.

ICML-2003-PavlovPPU #modelling

Mixtures of Conditional Maximum Entropy Models (DP, AP, DMP, LHU), pp. 584–591.

ICML-2003-SiJ #collaboration #flexibility

Flexible Mixture Model for Collaborative Filtering (LS, RJ), pp. 704–711.

ICML-2003-WangSPZ #learning #modelling #principle

Learning Mixture Models with the Latent Maximum Entropy Principle (SW, DS, FP, YZ), pp. 784–791.

KDD-2003-ChudovaGMS #clustering #invariant #modelling

Translation-invariant mixture models for curve clustering (DC, SG, EM, PS), pp. 79–88.

MLDM-2003-BouguilaZV #classification #image #novel

Novel Mixtures Based on the Dirichlet Distribution: Application to Data and Image Classification (NB, DZ, JV), pp. 172–181.

ECIR-2002-Lavrenko #information retrieval #modelling

Optimal Mixture Models in IR (VL), pp. 193–212.

ICPR-v2-2002-Abd-AlmageedS #modelling #statistics

Mixture Models for Dynamic Statistical Pressure Snakes (WAA, CES), pp. 721–724.

ICPR-v2-2002-GibsonCT #abstraction #modelling #using #visual notation

Visual Abstraction of Wildlife Footage Using Gaussian Mixture Models and the Minimum Description Length Criterion (DPG, NWC, BTT), pp. 814–817.

ICPR-v2-2002-KimKB #recognition #using

Face Recognition Using LDA Mixture Model (HCK, DK, SYB), pp. 486–489.

ICPR-v2-2002-LudtkeLHW #detection #using

Corner Detection Using a Mixture Model of Edge Orientation (NL, BL, ERH, RCW), pp. 574–577.

ICPR-v2-2002-OthmanA #2d #recognition

A Tied-Mixture 2-D HMM Face Recognition System (HO, TA), pp. 453–456.

KDD-2002-UedaS #category theory #detection #modelling #multi #parametricity #using

Single-shot detection of multiple categories of text using parametric mixture models (NU, KS), pp. 626–631.

SAT-2002-Hoos #algorithm #modelling #satisfiability

SLS algorithms for SAT: irregular instances, search stagnation, and mixture models (HH), p. 41.

ICDAR-2001-ZhangDZ #orthogonal #recognition

Offline Handwritten Character Recognition Based on Discriminative Training of Orthogonal Gaussian Mixture Model (RZ, XD, JZ), pp. 221–225.

STOC-2001-SanjeevK #learning

Learning mixtures of arbitrary gaussians (SA, RK), pp. 247–257.

CIKM-2001-ToutanovaCPH #classification #set

Text Classification in a Hierarchical Mixture Model for Small Training Sets (KT, FC, KP, TH), pp. 105–112.

ICML-2001-PellegM #clustering

Mixtures of Rectangles: Interpretable Soft Clustering (DP, AWM), pp. 401–408.

ICML-2001-SandM #estimation #modelling #using

Repairing Faulty Mixture Models using Density Estimation (PS, AWM), pp. 457–464.

ICML-2001-SeldinBT #markov #memory management #segmentation #sequence

Unsupervised Sequence Segmentation by a Mixture of Switching Variable Memory Markov Sources (YS, GB, NT), pp. 513–520.

ICML-2000-McLachlanP

Mixtures of Factor Analyzers (GJM, DP), pp. 599–606.

ICPR-v1-2000-GrossYW #invariant #modelling #recognition

Growing Gaussian Mixture Models for Pose Invariant Face Recognition (RG, JY, AW), pp. 5088–5091.

ICPR-v1-2000-Wilson #modelling #multi #named

MGMM: Multiresolution Gaussian Mixture Models for Computer Vision (RW), pp. 1212–1215.

ICPR-v2-2000-BradleyRF #clustering #database #modelling #scalability #using

Clustering Very Large Databases Using EM Mixture Models (PSB, CR, UMF), pp. 2076–2080.

ICPR-v2-2000-DahmenKGN #image #invariant #recognition #using

Invariant Image Object Recognition Using Mixture Densities (JD, DK, MOG, HN), pp. 2614–2617.

ICPR-v2-2000-FigueiredoJ #estimation #finite #modelling

Unsupervised Selection and Estimation of Finite Mixture Models (MATF, AKJ), pp. 2087–2090.

ICPR-v2-2000-GrimPS #multi #recognition

Multivariate Structural Bernoulli Mixtures for Recognition of Handwritten Numerals (JG, PP, PS), pp. 2585–2589.

ICPR-v2-2000-HammoudM #recognition #video

Mixture Densities for Video Objects Recognition (RIH, RM), pp. 2071–2075.

ICPR-v2-2000-Sanchez-Reillo #geometry #modelling #pattern matching #pattern recognition #recognition

Hand Geometry Pattern Recognition through Gaussian Mixture Modeling (RSR), pp. 2937–2940.

ICPR-v2-2000-ZwartK #modelling

Constrained Mixture Modeling of Intrinsically Low-Dimensional Distributions (JPZ, BJAK), pp. 2610–2613.

ICPR-v3-2000-OrWLL #modelling #segmentation #using #video

Panoramic Video Segmentation Using Color Mixture Models (SHO, KhW, KsL, TkL), pp. 3391–3394.

KDD-2000-YamanishiTWM #algorithm #detection #finite #learning #online #using

On-line unsupervised outlier detection using finite mixtures with discounting learning algorithms (KY, JiT, GJW, PM), pp. 320–324.

KDD-1999-GaffneyS #clustering #modelling

Trajectory Clustering with Mixtures of Regression Models (SG, PS), pp. 63–72.

ICPR-1998-GrimNPSF

Initializing normal mixtures of densities (JG, JN, PP, PS, FJF), pp. 886–890.

ICPR-1998-HofmannP #modelling

Mixture models for co-occurrence and histogram data (TH, JP), pp. 192–194.

ICPR-1998-KudoTSS #pattern matching #pattern recognition #recognition #subclass

A subclass-based mixture model for pattern recognition (MK, HT, SS, MS), pp. 870–872.

ICPR-1998-Kwok #classification #problem

Support vector mixture for classification and regression problems (JTYK), pp. 255–258.

ICPR-1998-McLachlanP #algorithm #automation #modelling #named #testing

MIXFIT: an algorithm for the automatic fitting and testing of normal mixture models (GJM, DP), pp. 553–557.

ICPR-1998-SardoK #complexity #estimation #using #validation

Model complexity validation for PDF estimation using Gaussian mixtures (LS, JK), pp. 195–197.

ICML-1996-SahamiHS #categorisation #model-to-text #multi

Applying the Multiple Cause Mixture Model to Text Categorization (MS, MAH, ES), pp. 435–443.

ICPR-1996-KasprzakC #analysis #component #image #independence

Hidden image separation from incomplete image mixtures by independent component analysis (WK, AC), pp. 394–398.

ICPR-1996-RamamurtiG #adaptation

Structural adaptation in mixture of experts (VR, JG), pp. 704–708.

KDD-1996-Feelders #learning #modelling #using

Learning from Biased Data Using Mixture Models (AJF), pp. 102–107.

KDD-1996-KontkanenMT #data mining #finite #mining #predict

Predictive Data Mining with Finite Mixtures (PK, PM, HT), pp. 176–182.

ICML-1995-Sutton #modelling

TD Models: Modeling the World at a Mixture of Time Scales (RSS), pp. 531–539.