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.