The DOI matches its Crossref record; 32 of the 33 references it cites were found in a registry or an index.
The Hierarchical Hidden Markov Model: Analysis and Applications
Open accessOverview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Fine et al. [10] were the first to introduce the HHMM, generalizing the HMM by viewing each state as an autonomous probabilistic HMM model itself.
“Fine et al. [10] were the first to introduce the HHMM, generalizing the HMM by viewing each state as an autonomous probabilistic HMM model itself.”
From Efficient duration and hierarchical modeling for human activity recognition (2009) · cites this paper as [10]
In a two-layer HHMM, the duration of a bottom-level state i in ch(p) follows a geometric distribution.
“In this two-layer HHMM, the duration of a bottom-level state i ∈ ch(p), denoted as D p,i , follows a geometric distribution.”
From Efficient duration and hierarchical modeling for human activity recognition (2009) · cites this paper as [10]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
Elements of Information Theory
Cover, T. M., & Thomas, J. A. (2001) · Wiley eBooks · doi:10.1002/0471200611 · Cited by 37,139
A tutorial on hidden Markov models and selected applications in speech recognition
Rabiner, L. R. (1989) · Proceedings of the IEEE · doi:10.1109/5.18626 · Cited by 22,743
Error bounds for convolutional codes and an asymptotically optimum decoding algorithm
Viterbi, A. J. (1967) · IEEE Transactions on Information Theory · doi:10.1109/tit.1967.1054010 · Cited by 6,954
An introduction to hidden Markov models
Rabiner, L., & Juang, B. (1986) · IEEE ASSP Magazine · doi:10.1109/massp.1986.1165342 · Cited by 4,775
Statistical Inference for Probabilistic Functions of Finite State Markov Chains
Baum, L. E., & Petrie, T. (1966) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177699147 · Cited by 2,775
Unsupervised word sense disambiguation rivaling supervised methods
Yarowsky, D. (1995) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.3115/981658.981684 · Cited by 2,411
A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition
Rabiner, L. R. (1990) · Elsevier eBooks · doi:10.1016/b978-0-08-051584-7.50027-9 · Cited by 1,945
Elements of Information Theory
Phoenix, S. J. D. (1992) · Journal of Modern Optics · doi:10.1080/09500349214551641 · Cited by 1,442
Factorial Hidden Markov Models
Ghahramani, Z., & Jordan, M. I. (1997) · Machine Learning · doi:10.1023/a:1007425814087 · Cited by 1,157
Unsupervised Models for Named Entity Classification
Collins, M., & Singer, Y. (1999) · Cited by 819
Self-citationApplications of stochastic context-free grammars using the Inside-Outside algorithm
Lari, K., & Young, S. J. (1991) · Computer Speech & Language · doi:10.1016/0885-2308(91)90009-f · Cited by 639
The Power of Amnesia: Learning Probabilistic Automata with Variable Memory Length
Ron, D., Singer, Y., & Tishby, N. (1996) · Machine Learning · doi:10.1007/bf00114008 · Cited by 490
Self-citationHidden Markov models of biological primary sequence information.
Baldi, P., Chauvin, Y., Hunkapiller, T., & McClure, M. A. (1994) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.91.3.1059 · Cited by 451
SELF-ORGANIZED LANGUAGE MODELING FOR SPEECH RECOGNITION
Jelinek, F. (1990) · Elsevier eBooks · doi:10.1016/b978-0-08-051584-7.50045-0 · Cited by 432
Robust part-of-speech tagging using a hidden Markov model
Kupiec, J. M. (1992) · Computer Speech & Language · doi:10.1016/0885-2308(92)90019-z · Cited by 425
The estimation of stochastic context-free grammars using the Inside-Outside algorithm
Lari, K., & Young, S. J. (1990) · Computer Speech & Language · doi:10.1016/0885-2308(90)90022-x · Cited by 393
A hidden Markov model that finds genes inE.coliDNA
Krogh, A., Mian, S. I., & Haussler, D. H. (1994) · Nucleic Acids Research · doi:10.1093/nar/22.22.4768 · Cited by 315
An Input Output HMM Architecture
Bengio, Y., & Frasconi, P. (1994) · Cited by 268
Factorial Hidden Markov Models.
Zoubin Ghahramani, A., & Jordan, M. I. (1996) · doi:10.21236/ada307097 · Cited by 256
Complexity of strings in the class of Markov sources
Rissanen, J. (1986) · IEEE Transactions on Information Theory · doi:10.1109/tit.1986.1057210 · Cited by 186
The Power of Amnesia: Learning Probabilistic Automata with Variable Memory Length
Ron, D., Singer, Y., & Tishby, N. (1996) · Machine Learning · doi:10.1023/a:1026490906255 · Cited by 185
Self-citationON THE COMPUTATIONAL COMPLEXITY OFAPPROXIMATING DISTRIBUTIONS BY PROBABILISTIC AUTOMATA
Abe, N., & Warmuth, M. K. (1990) · doi:10.5555/92571.92587 · Cited by 147
Best-first Model Merging for Hidden Markov Model InductionPreprint
Stolcke, A., & Omohundro, S. M. (1994) · arXiv (Cornell University) · doi:10.48550/arxiv.cmp-lg/9405017 · Cited by 116
Script recognition using hidden Markov models
Nag, R., Wong, K.-M., & Fallside, F. (2005) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1986.1168951 · Cited by 98
Markov Source Modeling of Text Generation
Jelinek, F. (1985) · Springer eBooks · doi:10.1007/978-94-009-5113-6_28 · Cited by 81
On the Computational Complexity of Approximating Distributions by Probabilistic Automata
Abe, N., & Warmuth, M. K. (1992) · Machine Learning · doi:10.1007/bf00992677 · Cited by 75
Dynamical encoding of cursive handwriting
Singer, Y., & Tishby, N. (1994) · Biological Cybernetics · doi:10.1007/bf00202762 · Cited by 57
Self-citationGat, I., Tishby, N., & Abeles, M. (1997) · Network Computation in Neural Systems · doi:10.1088/0954-898x_8_3_005 · Cited by 44
Self-citationTraining Algorithms for Hidden Markov Models using Entropy Based Distance Functions
Singer, Y., & Warmuth, M. K. (1996) · Cited by 38
Self-citationInference and minimization of hidden Markov chains
Gillman, D. W., & Sipser, M. (1994) · Conference on Learning Theory · doi:10.1145/180139.181091 · Cited by 19
Dynamical encoding of cursive handwriting
Singer, Y., & Tishby, N. (1994) · Biological Cybernetics · doi:10.1007/s004220050085 · Cited by 17
Self-citation
Flagged first, then by citations
1 of the 33 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Deep learning in neural networks: an overview
Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570
Dynamic bayesian networks: representation, inference and learning
Murphy, K. P., & Russell, S. (2002) · Cited by 2,579
Stochastic Variational InferencePreprint
Hoffman, M. D., Blei, D. M., Wang, C., & Paisley, J. W. (2012) · arXiv (Cornell University) · doi:10.5555/2567709.2502622 · Cited by 1,991
Deep Learning: Methods and Applications
Deng, L. (2014) · now publishers, Inc. eBooks · doi:10.1561/9781601988157 · Cited by 1,789
Bursty and Hierarchical Structure in Streams
Kleinberg, J. M. (2003) · Data Mining and Knowledge Discovery · doi:10.1023/a:1024940629314 · Cited by 1,372
Bursty and hierarchical structure in streams
Kleinberg, J. M. (2002) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/775047.775061 · Cited by 1,182
Recent Advances in Hierarchical Reinforcement Learning
Barto, A. G., & Mahadevan, S. (2003) · Discrete Event Dynamic Systems · doi:10.1023/a:1022140919877 · Cited by 1,059
A tutorial survey of architectures, algorithms, and applications for deep learning
Deng, L. (2014) · APSIPA Transactions on Signal and Information Processing · doi:10.1017/atsip.2013.9 · Cited by 747
Active Hidden Markov Models for Information Extraction
Scheffer, T., Decomain, C., & Wrobel, S. (2001) · Lecture notes in computer science · doi:10.1007/3-540-44816-0_31 · Cited by 576
Activity Recognition and Abnormality Detection with the Switching Hidden Semi-Markov Model
Duong, T. V., Bui, H. H., Phung, D. Q., & Venkatesh, S. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.61 · Cited by 535
Kiani, R., Hanks, T. D., & Shadlen, M. N. (2008) · The Journal of Neuroscience · doi:10.1523/jneurosci.4761-07.2008 · Cited by 500
Procedural Content Generation via Machine Learning (PCGML)
Summerville, A., Snodgrass, S., Guzdial, M., Holmgård, C., Hoover, A. K., Isaksen, A., Nealen, A., & Togelius, J. (2018) · IEEE Transactions on Games · doi:10.1109/tg.2018.2846639 · Cited by 495
Situation identification techniques in pervasive computing: A review
Ye, J., Dobson, S., & McKeever, S. (2011) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2011.01.004 · Cited by 453
HHMM-based Chinese lexical analyzer ICTCLAS
Zhang, H., Yu, H., Xiong, D., & Liu, Q. (2003) · SIGHAN Workshop on Chinese Language Processing (SIGHAN) · doi:10.3115/1119250.1119280 · Cited by 452
A Systematic Review of Hidden Markov Models and Their Applications
Mor, B., Garhwal, S., & Kumar, A. (2020) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-020-09422-4 · Cited by 381
A Survey on Multimodal Large Language Models for Autonomous Driving
Cui, C., Ma, Y., Cao, X., Ye, W., Zhou, Y., Liang, K., Chen, J., Lu, J., Yang, Z., Liao, K.-D., et al. (2024) · 2024 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw60836.2024.00106 · Cited by 369
Catching the Drift: Probabilistic Content Models, with Applications to Generation and SummarizationPreprint
Barzilay, R., & Lee, L. (2004) · arXiv (Cornell University) · doi:10.48550/arxiv.cs/0405039 · Cited by 362
Conditional random fields for activity recognition
Vail, D. L., Veloso, M. M., & Lafferty, J. D. (2007) · Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems · doi:10.1145/1329125.1329409 · Cited by 357
Blockchain and Machine Learning for Communications and Networking Systems
Liu, Y., Yu, F. R., Li, X., Ji, H., & Leung, V. C. M. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2975911 · Cited by 332
Nguyen, N., Phung, D., Venkatesh, S., & Bui, H. (2005) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2005.203 · Cited by 324
Real-time human action recognition based on depth motion maps
Chen, C., Liu, K., & Kehtarnavaz, N. (2013) · Journal of Real-Time Image Processing · doi:10.1007/s11554-013-0370-1 · Cited by 321
Recent Advances in Hierarchical Reinforcement Learning
Barto, A. G., & Mahadevan, S. (2003) · Discrete Event Dynamic Systems · doi:10.1023/a:1025696116075 · Cited by 317
Layered representations for learning and inferring office activity from multiple sensory channels
Oliver, N., Garg, A., & Horvitz, E. (2004) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2004.02.004 · Cited by 316
HMMs for diagnostics and prognostics in machining processes
Baruah, P., & Chinnam, R. B. (2005) · International Journal of Production Research · doi:10.1080/00207540412331327727 · Cited by 309
Sutton, C. A., McCallum, A., & Rohanimanesh, K. (2007) · ScholarWorks@UMassAmherst (University of Massachusetts Amherst) · doi:10.1145/1015330.1015422 · Cited by 291
Kogan, J. A., & Margoliash, D. (1998) · The Journal of the Acoustical Society of America · doi:10.1121/1.421364 · Cited by 282
Using GANs for Sharing Networked Time Series Data
Lin, Z., Jain, A., Wang, C., Fanti, G. C., & Sekar, V. (2020) · ACM Internet Measurement Conference (IMC) · doi:10.1145/3419394.3423643 · Cited by 271
Uncovering ecological state dynamics with hidden Markov models
McClintock, B. T., Langrock, R., Giménez, O., Cam, E., Borchers, D. L., Glennie, R., & Patterson, T. A. (2020) · Ecology Letters · doi:10.1111/ele.13610 · Cited by 264
Continuous Online Sequence Learning with an Unsupervised Neural Network Model
Cui, Y., Ahmad, S., & Hawkins, J. (2016) · Neural Computation · doi:10.1162/neco_a_00893 · Cited by 261
Finding approximate POMDP solutions through belief compression
Roy, N., Gordon, G., & Thrun, S. (2005) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1496 · Cited by 254
Sohal, A. S., Sandhu, R., Sood, S. K., & Chang, V. I. C. (2017) · Computers & Security · doi:10.1016/j.cose.2017.08.016 · Cited by 252
Keyboard acoustic emanations revisited
Li, Z., Zhou, F., & Tygar, J. D. (2009) · ACM Transactions on Information and System Security · doi:10.1145/1609956.1609959 · Cited by 251
ASRNN: A recurrent neural network with an attention model for sequence labeling
Lin, J. C., Shao, Y., Djenouri, Y., & Yun, U. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.106548 · Cited by 238
Contextual LSTM (CLSTM) models for Large scale NLP tasksPreprint
Ghosh, S., Vinyals, O., Strope, B. P., Roy, S., Dean, T., & Heck, L. P. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1602.06291 · Cited by 236
Hoey, J., Poupart, P., Bertoldi, A. V., Craig, T., Boutilier, C., & Mihailidis, A. (2010) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2009.06.008 · Cited by 229
A hierarchical bayesian model of invariant pattern recognition in the visual cortex
George, D., & Hawkins, J. (2006) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2005.1556155 · Cited by 223
Lavee, G., Rivlin, E., & Rudzsky, M. (2009) · IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) · doi:10.1109/tsmcc.2009.2023380 · Cited by 217
Combining distributional and morphological information for part of speech induction
Clark, A. (2003) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.3115/1067807.1067817 · Cited by 208
Named entity recognition with character-level models
Klein, D., Smarr, J., Nguyễn, V. H., & Manning, C. D. (2003) · Conference on Computational Natural Language Learning · doi:10.3115/1119176.1119204 · Cited by 196
A general model for online probabilistic plan recognition
Bui, H. (2003) · Cited by 190
Ahmed, N., Amin, R., Aldabbas, H., Koundal, D., Alouffi, B., & Shah, T. (2022) · Security and Communication Networks · doi:10.1155/2022/1862888 · Cited by 190
Linear-time inference in Hierarchical HMMs
Murphy, K. P., & Paskin, M. A. (2001) · Cited by 188
Analysis of animal accelerometer data using hidden Markov models
Leos‐Barajas, V., Photopoulou, T., Langrock, R., Patterson, T. A., Watanabe, Y. Y., Murgatroyd, M., & Papastamatiou, Y. P. (2017) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.12657 · Cited by 187
Shi, S. W., Wedel, M., & Pieters, F. G. M. (2012) · Management Science · doi:10.1287/mnsc.1120.1625 · Cited by 181
Host-Based Intrusion Detection System with System Calls
Liu, M., Xue, Z., Xu, X., Zhong, C., & Chen, J. (2018) · ACM Computing Surveys · doi:10.1145/3214304 · Cited by 180
Continuous Realtime Gesture Following and Recognition
Bevilacqua, F. P., Zamborlin, B., Sypniewski, A., Schnell, N., Guédy, F., & Rasamimanana, N. H. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-12553-9_7 · Cited by 177
Simultaneous record detection and attribute labeling in web data extraction
Zhu, J., Nie, Z., Wen, J.-R., Zhang, B., & Ma, W. (2006) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1150402.1150457 · Cited by 171
Parsing Videos of Actions with Segmental Grammars
Pirsiavash, H., & Ramanan, D. (2014) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2014.85 · Cited by 160
Plan, Activity, and Intent Recognition
Sukthankar, G., Geib, C., Bui, H., Pynadath, D. V., & Goldman, R. P. (2014) · Elsevier eBooks · doi:10.1016/c2012-0-01169-6 · Cited by 158
Health-State Estimation and Prognostics in Machining Processes
Camci, F., & Chinnam, R. B. (2010) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2009.2038170 · Cited by 157
The use of pervasive sensing for behaviour profiling — a survey
Atallah, L., & Yang, G. (2009) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2009.06.009 · Cited by 155
Context-Sensitive Learning for Enhanced Audiovisual Emotion Classification
Metallinou, A., Wöllmer, M., Katsamanis, A., Eyben, F., Schuller, B. W., & Narayanan, S. S. (2012) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2011.40 · Cited by 154
Semantic Representation and Recognition of Continued and Recursive Human Activities
Ryoo, M. S., & Aggarwal, J. K. (2008) · International Journal of Computer Vision · doi:10.1007/s11263-008-0181-1 · Cited by 153
Keyboard acoustic emanations revisited
Li, Z., Zhou, F., & Tygar, J. D. (2005) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/1102120.1102169 · Cited by 150
A Hierarchical Hidden Markov Model Framework for Home Appliance Modeling
Kong, W., Dong, Z. Y., Hill, D. J., Ma, J., Zhao, J., & Luo, F. (2016) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2016.2626389 · Cited by 147
Coupled Hidden Semi Markov Models for Activity Recognition
Natarajan, P., & Nevatia, R. (2007) · 2007 IEEE Workshop on Motion and Video Computing (WMVC'07) · doi:10.1109/wmvc.2007.12 · Cited by 144
MAppleT: simulation of apple tree development using mixed stochastic and biomechanical models
Costes, E., Smith, C. R., Renton, M., Guédon, Y., Prusinkiewicz, P., & Godin, C. (2008) · Functional Plant Biology · doi:10.1071/fp08081 · Cited by 143
Hierarchical hidden Markov models for information extraction
Skounakis, M., Craven, M. W., & Ray, S. (2003) · Cited by 142
Matsubara, Y., Sakurai, Y., & Faloutsos, C. (2014) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/2588555.2588556 · Cited by 141
A survey on machine learning in Internet of Things: Algorithms, strategies, and applications
Messaoud, S., Bradai, A., Bukhari, S. H. R., Quang, P. T. A., Ahmed, O. B., & Atri, M. (2020) · Internet of Things · doi:10.1016/j.iot.2020.100314 · Cited by 135
Semantic processing using the Hidden Vector State model
He, Y., & Young, S. J. (2004) · Computer Speech & Language · doi:10.1016/j.csl.2004.03.001 · Cited by 132
Data Mining for Hierarchical Model Creation
Youngblood, G. M., & Cook, D. J. (2007) · IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) · doi:10.1109/tsmcc.2007.897341 · Cited by 131
A daily behavior enabled hidden Markov model for human behavior understanding
Chung, P.-C., & Liu, C.-D. (2008) · Pattern Recognition · doi:10.1016/j.patcog.2007.10.022 · Cited by 130
Efficient Training of Conditional Random Fields
Wallach, H. (2002) · Cited by 127
Managing Adaptive Versatile Environments
Youngblood, G. M., Holder, L. B., & Cook, D. J. (2005) · IEEE International Conference on Pervasive Computing and Communications · doi:10.1109/percom.2005.23 · Cited by 126
Chinese lexical analysis using hierarchical hidden Markov model
Zhang, H., Liu, Q., Cheng, X., Zhang, H., & Yu, H. (2003) · SIGHAN Workshop on Chinese Language Processing (SIGHAN) · doi:10.3115/1119250.1119259 · Cited by 125
Ouyang, F., Xu, W., & Cukurova, M. (2023) · International Journal of Computer-Supported Collaborative Learning · doi:10.1007/s11412-023-09387-z · Cited by 124
Hidden Markov models: Pitfalls and opportunities in ecology
Glennie, R., Adam, T., Barajas, V. L., Michelot, T., Photopoulou, T., & McClintock, B. T. (2022) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13801 · Cited by 124
Efficient duration and hierarchical modeling for human activity recognition
Duong, T., Phung, D., Bui, H., & Venkatesh, S. (2009) · Artificial Intelligence · doi:10.1016/j.artint.2008.12.005 · Cited by 115
Data Storage Management in Cloud Environments
Mansouri, Y., Toosi, A. N., & Buyya, R. (2017) · ACM Computing Surveys · doi:10.1145/3136623 · Cited by 115
A Review of HMM-Based Approaches of Driving Behaviors Recognition and Prediction
Deng, Q., & Söffker, D. (2021) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2021.3065933 · Cited by 113
Bayesian network model for semi-structured document classification
Denoyer, L., & Gallinari, P. (2004) · Information Processing & Management · doi:10.1016/j.ipm.2004.04.009 · Cited by 113
Three Classes of Deep Learning Architectures and Their Applications: A Tutorial Survey
Deng, L. (2012) · APSIPA Transactions on Signal and Information Processing · Cited by 111
Credit Card Fraud Detection using Machine Learning: A StudyPreprint
Tiwari, P., Mehta, S., Sakhuja, N., Kumar, J., & Singh, A. K. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.10005 · Cited by 109
Nie, Z., Wen, J.-R., & Ma, W. (2007) · Conference on Innovative Data Systems Research · Cited by 108
Shape-and-Behavior Encoded Tracking of Bee Dances
Veeraraghavan, A., Chellappa, R., & Srinivasan, M. V. (2008) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.70707 · Cited by 106
Peng, Y.-T., Lin, C., Sun, M., & Tsai, K. (2009) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2009.5202720 · Cited by 104
HHMMiR: efficient de novo prediction of microRNAs using hierarchical hidden Markov models
Kadri, S., Hinman, V. F., & Benos, P. V. (2009) · BMC Bioinformatics · doi:10.1186/1471-2105-10-s1-s35 · Cited by 101
A KEYHOLE PLAN RECOGNITION MODEL FOR ALZHEIMER'S PATIENTS: FIRST RESULTS
Bouchard, B., Giroux, S., & Bouzouane, A. (2007) · Applied Artificial Intelligence · doi:10.1080/08839510701492579 · Cited by 98
Spoken language understanding using the Hidden Vector State Model
He, Y., & Young, S. J. (2005) · Speech Communication · doi:10.1016/j.specom.2005.06.002 · Cited by 96
Kersting, K., Raedt, L. D., & Raiko, T. (2006) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1675 · Cited by 96
Sequence memory for prediction, inference and behaviour
Hawkins, J., George, D., & Niemasik, J. (2009) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2008.0322 · Cited by 91
Segmentation of Musical Signals Using Hidden Markov Models.
Sandler, M. B., & Aucouturier, J. (2001) · Cited by 88
Anomaly detection in GPS data based on visual analytics
Liao, Z., Yu, Y., & Chen, B. (2010) · IEEE Conference on Visual Analytics Science and Technology (VAST) · doi:10.1109/vast.2010.5652467 · Cited by 88
An Introduction to Infinite HMMs for Single-Molecule Data Analysis
Sgouralis, I., & Pressé, S. (2017) · Biophysical Journal · doi:10.1016/j.bpj.2017.04.027 · Cited by 87
Ghassemi, N. H., Hannink, J., Martindale, C. F., Gaßner, H., Müller, M., Klucken, J., & Eskofier, B. M. (2018) · Sensors · doi:10.3390/s18010145 · Cited by 86
Barajas, V. L., Gangloff, E. J., Adam, T., Langrock, R., van Beest, F. M., Nabe‐Nielsen, J., & Morales, J. M. (2017) · Journal of Agricultural Biological and Environmental Statistics · doi:10.1007/s13253-017-0282-9 · Cited by 84
Joint modelling of multi‐scale animal movement data using hierarchical hidden Markov models
Adam, T., Griffiths, C. A., Barajas, V. L., Meese, E. N., Lowe, C. G., Blackwell, P. G., Righton, D. A., & Langrock, R. (2019) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13241 · Cited by 83
(2004) · Deakin Research Online (Deakin University) · Cited by 82
Irie, G., Satou, T., Kojima, A., Yamasaki, T., & Aizawa, K. (2010) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2010.2051871 · Cited by 79
Motion intention recognition in robot assisted applications
Aarno, D., & Kragić, D. (2007) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2007.11.005 · Cited by 78
Studying crop sequences with CarrotAge, a HMM-based data mining software
Ber, F. L., Benoît, M., Schott, C., Mari, J., & Mignolet, C. (2005) · Ecological Modelling · doi:10.1016/j.ecolmodel.2005.08.031 · Cited by 78
Cont, A. (2006) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2006.1661258 · Cited by 78
Multimodal Human Activity Recognition for Industrial Manufacturing Processes in Robotic Workcells
Roitberg, A., Somani, N., Perzylo, A., Rickert, M., & Knoll, A. (2015) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/2818346.2820738 · Cited by 78
Artificial intelligence and empirical consumer research: A topic modeling analysis
Vaid, S., Puntoni, S., & Khodr, A. (2023) · Journal of Business Research · doi:10.1016/j.jbusres.2023.114110 · Cited by 77
Dao, T.-K., Nguyen, T.-T., Pan, J., Qiao, Y., & Lai, Q.-A. (2020) · IEEE Access · doi:10.1109/access.2020.2983219 · Cited by 68
Adapting Recommendations to Contextual Changes Using Hierarchical Hidden Markov Models
Aghdam, M. H., Hariri, N., Mobasher, B., & Burke, R. (2015) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/2792838.2799684 · Cited by 68
Automatic recognition of handwritten Arabic characters: a comprehensive review
Balaha, H. M., Ali, H. A., & Badawy, M. (2020) · Neural Computing and Applications · doi:10.1007/s00521-020-05137-6 · Cited by 68
Detecting and discriminating behavioural anomalies
Loy, C. C., Xiang, T., & Gong, S. (2010) · Pattern Recognition · doi:10.1016/j.patcog.2010.07.023 · Cited by 67
Ye, J., Stevenson, G., & Dobson, S. (2014) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/2662870 · Cited by 65
Metadata
- Authors
- Fine, S., Singer, Y., & Tishby, N.
- Affiliations
- Hebrew University of Jerusalem (IL)
- AT&T (United States) (US)
- Venue
- Machine Learning
- Published
- 1998-07
- Volume
- 32
- Issue
- 1
- Pages
- 41-62
- ISSN
- 0885-6125 (ISSN-L)
- 1573-0565
- OpenAlex
- W1636244751
- Cited by
- 906
- References
- 33
- Citations by year
- 2026: 10 · 2025: 17 · 2024: 19 · 2023: 26 · 2022: 39 · 2021: 36 · 2020: 51 · 2019: 34 · 2018: 30 · 2017: 39 · 2016: 33 · 2015: 31 · 2014: 53 · 2013: 36 · 2012: 43
- FWCI
- 3.53
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Machine Learning (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Methods and Mixture Models
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- hierarchical hidden Markov models, unsupervised parameter estimation, Baum-Welch algorithm, cursive script, natural language modeling
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
