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?
The cited source says what the claim attributes to it.
In the citing paper
“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]
The cited source says what the claim attributes to it.
In the citing paper
“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
Flagged first, then by citations
1 of the 33 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Items 501–600 of 928
UltraMotion: High-precision Ultrasonic Arm Tracking for Real-world Exercises
Niu, X., Zou, K., Shen, D., Drew, S., Wu, S., Guo, G., & Chen, R. (2023) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2023.3241077 · Cited by 5
Odrekhivskyі, M., Kohut, U., Kolomatskyi, V., Horbal, N., Wołowiec, T., & Dluhopolska, T. (2025) · Sustainability · doi:10.3390/su17083641 · Cited by 5
An EM Algorithm to Learn Sequences in the Wavelet Domain
Milone, D. H., & Persia, L. E. D. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-76631-5_49 · Cited by 5
Liu, Y., Kosut, O., & Willsky, A. S. (2014) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2014.2375134 · Cited by 5
Cheap noisy sensors can improve activity monitoring under stringent energy constraints
Jun, D. M., Le, L. N., & Jones, D. L. (2013) · IEEE Global Conference on Signal and Information Processing · doi:10.1109/globalsip.2013.6736983 · Cited by 5
Taming the Zoo of Discrete HMM Subspecies & Some of their Relatives
Christiansen, H., Theil, H. C., Torp, L. O., & Matthieu, P. (2011) · Frontiers in artificial intelligence and applications · doi:10.3233/978-1-60750-762-8-28 · Cited by 5
Temporal and Object Relations in Unsupervised Plan and Activity Recognition.
Freedman, R. G., Jung, H.-T., & Zilberstein, S. (2015) · Cited by 5
Time Series Analysis using a Kernel based Multi-Modal Uncertainty Decomposition Framework
Singh, R., & Príncipe, J. C. (2020) · Uncertainty in Artificial Intelligence · Cited by 5
An Urban Computing Framework for Autonomous Services in a U-City
Lee, M., Uhm, Y., Hwang, Z., Kim, Y., Jo, J., & Park, S. (2007) · International Conference on Computer Sciences and Convergence Information Technology · doi:10.1109/iccit.2007.129 · Cited by 5
Samko, O., Marshall, A. D., & Rosin, P. L. (2010) · International Conference on Computer Vision Theory and Applications · doi:10.5220/0002815202750280 · Cited by 5
Learning Profiles Based on Hierarchical Hidden Markov Model
Galassi, U., Giordana, A., Saitta, L., & Botta, M. (2005) · Lecture notes in computer science · doi:10.1007/11425274_5 · Cited by 5
Dynamic Gesture Recognition Based on Dynamic Bayesian Networks
Wang, W.-H. A. (2007) · Cited by 5
Techniques to incorporate the benefits of a hierarchy in a modified hidden Markov model
Chou, L.-Y. (2006) · International Conference on Computational Linguistics and Annual Meeting of the Association for Computational Linguistics (COLING/ACL) · doi:10.3115/1273073.1273089 · Cited by 5
The hidden vector state language model
Seneviratne, V., & Young, S. J. (2005) · Interspeech (USB) · doi:10.21437/interspeech.2005-5 · Cited by 5
Improving Bayesian Network Structure Search with Random Variable Aggregation Hierarchies
Burge, J., & Lane, T. D. R. (2006) · Lecture notes in computer science · doi:10.1007/11871842_11 · Cited by 5
Symbolic Modeling of Prosody: From Linguistics to Statistics
Obin, N., & Lanchantin, P. (2015) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2014.2387389 · Cited by 5
Learning the hierarchical structure of spatial environments using multiresolution statistical models
Theocharous, G., & Mahadevan, S. (2003) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/irds.2002.1041528 · Cited by 5
Mining massive hierarchical data using a scalable probabilistic graphical model
AlJadda, K., Korayem, M., Ortiz, C., Grainger, T., Miller, J. A., Rasheed, K., Kochut, K. J., Peng, H., York, W. S., Ranzinger, R., et al. (2017) · Information Sciences · doi:10.1016/j.ins.2017.10.014 · Cited by 5
Budgeted online collective inference
Pujara, J., London, B., & Getoor, L. C. (2015) · Uncertainty in Artificial Intelligence · Cited by 5
Automatic Semantic Annotation Using Machine Learning
Tang, J., Zhang, D., Yao, L., & Li, Y. (2010) · IGI Global eBooks · doi:10.4018/978-1-60566-028-8.ch006 · Cited by 5
Ego-centric traffic behavior understanding through multi-level vehicle trajectory analysis
Xu, D., Xu, H., Zhao, H., Cui, J., Zha, H., Guillemard, F., Géronimi, S., & Aioun, F. (2017) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2017.7989026 · Cited by 5
Using Belief Networks and Fisher Kernels for Structured Document Classification
Denoyer, L., & Gallinari, P. (2003) · Lecture notes in computer science · doi:10.1007/978-3-540-39804-2_13 · Cited by 5
Probabilistic graphical models for human interaction analysis
Zhang, D. (2006) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · doi:10.5075/epfl-thesis-3709 · Cited by 5
Unraveling Temporal Processes using Probabilistic Graphical Models
Bueno, M. L. P. (2020) · Leiden Repository (Leiden University) · doi:10.13140/rg.2.2.15251.84000 · Cited by 5
When the Guard failed the Droid: A case study of Android malwarePreprint
Berger, H., Hajaj, C., & Dvir, A. Z. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.14123 · Cited by 5
Identifying urban spatial clusters via flow dynamics: a coupled tensor-based method
Zhao, Z., Kan, Z., Kwan, M., & Tang, L. (2025) · International Journal of Digital Earth · doi:10.1080/17538947.2025.2525382 · Cited by 4
Using Markov Models to Mine Temporal and Spatial Data
Mari, J., Le, F., Lazrak, E.-G., Benot, M., Eng, C., Thibessard, A., & Leblo, P. (2011) · InTech eBooks · doi:10.5772/13720 · Cited by 4
Improving of handwritten Tunisian City names recognition based on Factorial Hidden Markov Model
Jayech, K., Mahjoub, M. A., & Amara, N. E. B. (2014) · IEEE International Conference on Image Processing, Applications and Systems (IPAS) · doi:10.1109/ipas.2014.7043273 · Cited by 4
Part-Of Speech Tagging Base on Hidden Markov Model
Wang, X. J., & Hu, S. Y. (2012) · Applied Mechanics and Materials · doi:10.4028/www.scientific.net/amm.198-199.852 · Cited by 4
Xie, Y., Hu, J., Tang, S., & Huang, X. (2012) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2012.08.010 · Cited by 4
Credal human activity recognition based-HMM by combining hierarchical and temporal reasoning
Ahouandjinou, A., Motamed, C., Ezin, E. C., & Pinti, A. (2015) · International Conference on Image Processing Theory, Tools and Applications (IPTA) · doi:10.1109/ipta.2015.7367094 · Cited by 4
Scene Understanding Based on Sound and Text Information for a Cooking Support Robot
Kojima, R., Sugiyama, O., & Nakadai, K. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-19066-2_64 · Cited by 4
Automatic Chord Recognition with Higher-Order Harmonic Language Modelling
Korzeniowski, F., & Widmer, G. (2018) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco.2018.8553600 · Cited by 4
Combination of graphics, uncertainty, and semantics: A survey
Gao, Y., & Rafi, M. A. (2021) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.6711 · Cited by 4
Personalizing a Service Robot by Learning Human Habits from Behavioral Footprints
Li, K., & Meng, M. Q. ‐. (2015) · Engineering · doi:10.15302/j-eng-2015024 · Cited by 4
Analysis of the changes in listening trends of a music streaming service
Takano, M., Mizukami, H., Toriumi, F., Takeuchi, M., Wada, K., Yasuda, M., & Fukiida, I. (2017) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2017.8258290 · Cited by 4
A Computer Science Perspective on Models of the Mind
Brooks, T. N., Kamruzzaman, A., Leider, A. M., & Tappert, C. C. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-01057-7_57 · Cited by 4
Manuweera, B., Reynolds, G. L., & Kahanda, I. (2019) · PeerJ Computer Science · doi:10.7717/peerj-cs.233 · Cited by 4
Tagging Reading Comprehension Materials With Document Extraction Attention Networks
Sun, B., Zhu, Y., Zeng, Y., Xiao, R., Xiao, Y., & Wei, Y. (2020) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2020.2990724 · Cited by 4
Hidden Markov models for multi-scale time series : an application to stock market data
Adam, T., & Oelschläger, L. (2020) · St Andrews Research Repository (St Andrews Research Repository) · Cited by 4
Particle Swarm Optimization in Bioinformatics, Image Processing, and Computational Linguistics
Soni, B., Roy, S., & Warsi, S. (2021) · International Journal of Swarm Intelligence Research · doi:10.4018/ijsir.2021100102 · Cited by 4
Incorporating time-interval sequences in linear TV for next-item prediction
Bogina, V., Variat, Y., Kuflik, T., & Dim, E. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.116284 · Cited by 4
A Physics-informed Latent Variables of Corrosion Growth in Oil and Gas Pipelines
Huynh, P. K., Alqarni, A. A., Yadav, O. P., & Le, T. (2023) · Annual Reliability and Maintainability Symposium (RAMS) · doi:10.1109/rams51473.2023.10088241 · Cited by 4
Abalearn: A Risk-Sensitive Approach to Self-play Learning in Abalone
Campos, P., & Langlois, T. (2003) · Lecture notes in computer science · doi:10.1007/978-3-540-39857-8_6 · Cited by 4
Embedding HMM’s-based models in a Euclidean space: The topological hidden Markov models
Bouchaffra, D. (2008) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2008.4761135 · Cited by 4
Si, H., Saruwatari, S., Minami, M., & Morikawa, H. (2010) · Journal of Information Processing · doi:10.2197/ipsjjip.18.147 · Cited by 4
A Two-Layer Hidden Markov Model for the Arrival Process of Web Traffic
Xie, Y., Yu, S., Tang, S., & Xiang-nong, H. (2011) · Proceedings - International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems · doi:10.1109/mascots.2011.18 · Cited by 4
Multi-Dimensional Dependency-Tree Hidden Markov Models
Mérialdo, B., Jitén, J., & Huet, B. (2006) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2006.1660457 · Cited by 4
Shin, J., Shin, D., & Shin, D. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-38027-3_66 · Cited by 4
Spatiotemporal Abstraction of Stochastic Sequential Processes
Mahadevan, S. (2002) · Lecture notes in computer science · doi:10.1007/3-540-45622-8_3 · Cited by 4
A Logical Hierarchical Hidden Semi-Markov Model for Team Intention Recognition
Yue, S., Jiao, P., Zha, Y., & Yin, Q. (2015) · Discrete Dynamics in Nature and Society · doi:10.1155/2015/975951 · Cited by 4
Possibilistic reasoning effects on Hidden Markov Models effectiveness
Elbahi, A., Omri, M. N., & Mahjoub, M. A. (2015) · IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) · doi:10.1109/fuzz-ieee.2015.7338045 · Cited by 4
The Semantic Web for Knowledge and Data Management
(2009) · IGI Global eBooks · doi:10.4018/978-1-60566-028-8 · Cited by 4
An Exponential Representation in the API Algorithm for Hidden Markov Models Training
Aupetit, S., Monmarché, N., Slimane, M., & Liardet, P. (2006) · Lecture notes in computer science · doi:10.1007/11740698_6 · Cited by 4
Hierarchical Hidden Markov Model for Rushes Structuring and Indexing
Ngo, C., Pan, Z., & Wei, X.-Y. (2006) · Lecture notes in computer science · doi:10.1007/11788034_25 · Cited by 4
Liu, M. (2014) · DukeSpace (Duke University) · Cited by 4
Decision theoretic learning of human facial displays and gestures
Hoey, J. (2009) · Open Collections · doi:10.14288/1.0051546 · Cited by 4
Visualisation of structured data through generative probabilistic modeling
Gianniotis, N. (2008) · University of Birmingham Institutional Research Archive (University of Birmingham) · Cited by 4
Managing and analyzing carbohydrate data
Aoki-Kinoshita, K. F., Ueda, N., Yamaguchi, A., Akutsu, T., Kanehisa, M. I., & Mamitsuka, H. (2004) · ACM SIGMOD Record · doi:10.1145/1024694.1024700 · Cited by 4
A computational framework for unsupervised analysis of everyday human activities
Bobick, A., & Hamid, M. I. (2008) · SMARTech Repository (Georgia Institute of Technology) · Cited by 4
Classical Music Composition Using Hidden Markov Models
Yanchenko, A. K. (2017) · DukeSpace (Duke University) · Cited by 4
Untangling Sequences: Behavior vs. External CausesPreprint
Ahmad, S., & Hawkins, J. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/190678 · Cited by 4
Hidden Markov Models Applied To Intraday Momentum Trading With Side InformationPreprint
Christensen, H. L., Godsill, S., & Turner, R. E. (2020) · RePEc: Research Papers in Economics · doi:10.48550/arxiv.2006.08307 · Cited by 4
Dynamic Predictive Coding with HypernetworksPreprint
Jiang, L. P., Gklezakos, D. C., & Rao, R. P. N. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.02.22.432194 · Cited by 4
Predictive representations for sequential decision making under uncertainty
Boularias, A. (2010) · MPG.PuRe (Max Planck Society) · Cited by 4
Probabilistic-based scheduling for runtime goal sequence of agents
Song, H., Shen, Z., Yu, H., & Chen, Y. (2012) · IEEE International Conference on Computer Science and Automation Engineering · doi:10.1109/csae.2012.6272999 · Cited by 3
Phishing short URL detection based on link jumping on social networks
Xie, B., Li, Q., & Wei, N. (2022) · ITM Web of Conferences · doi:10.1051/itmconf/20224701009 · Cited by 3
A New Probabilistic Model for Recognizing Signs with Systematic Modulations
Ong, S. C. W., & Ranganath, S. (2007) · Springer eBooks · doi:10.1007/978-3-540-75690-3_2 · Cited by 3
How Robots Can Recognize Activities and Plans Using Topic Models
Freedman, R. G., Jung, H.-T., Grupen, R. A., & Zilberstein, S. (2014) · Cited by 3
Extraction of Hierarchical Behavior Patterns Using a Non-parametric Bayesian Approach
Briones, J. C., Kubo, T., & Ikeda, K. (2020) · Frontiers in Computer Science · doi:10.3389/fcomp.2020.546917 · Cited by 3
Neural Prediction of Lagrangian Drift Trajectories on the Sea Surface
Botvynko, D., Granero-Belinchón, C., van Gennip, S. J., Benzinou, A., & Fablet, R. (2025) · Artificial Intelligence for the Earth Systems · doi:10.1175/aies-d-24-0052.1 · Cited by 3
Bryson, J. J. (2012) · Palgrave Macmillan UK eBooks · doi:10.1057/9780230354456_7 · Cited by 3
A short text topic discovery method for social network
Liu, J., Wang, Q., Liu, Y., & Li, Y. (2014) · Chinese Control Conference (CCC) · doi:10.1109/chicc.2014.6896676 · Cited by 3
Recognising Human Behaviour in a Spatio-Temporal Context
Guesgen, H. W., & Marsland, S. (2011) · Advances in computational intelligence and robotics book series · doi:10.4018/978-1-61692-857-5.ch022 · Cited by 3
Joint entropy-based motion segmentation for 3D animations
Luo, G., Lei, G., Cao, Y., Liu, Q., & Seo, H. (2016) · The Visual Computer · doi:10.1007/s00371-016-1313-1 · Cited by 3
Memory Segment Matching Network Based Image Geo-Localization
Chen, J., Duan, Y., Sobelman, G. E., & Zhang, C. (2019) · IEEE Access · doi:10.1109/access.2019.2922378 · Cited by 3
Game Theory Based Sensor Management in Reducing Target Threat Level Assessment Risk
Pang, C., & Shan, G. (2021) · Journal of Shanghai Jiaotong University (Science) · doi:10.1007/s12204-021-2372-7 · Cited by 3
Behavioral evidence of a harmonic surprise effect on preference in popular music
Miles, S. A., Rosen, D. S., & Grzywacz, N. M. (2022) · Current Research in Behavioral Sciences · doi:10.1016/j.crbeha.2022.100080 · Cited by 3
Remaining Useful Life Prediction Based on Improved LSTM Hybrid Attention Neural Network
Xu, M., Bai, Y., & Qian, P. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-13832-4_58 · Cited by 3
Speech Recognition Algorithms based Cough Recognition System
Barkani, F., Hamidi, M., Zealouk, O., & Satori, H. (2023) · International Journal of Online and Biomedical Engineering (iJOE) · doi:10.3991/ijoe.v19i12.40471 · Cited by 3
Cao, Q., Liu, Y., Ren, G., Wang, S., Li, D., Deng, Y., & Qu, X. (2024) · Transportation Research Part A Policy and Practice · doi:10.1016/j.tra.2024.104264 · Cited by 3
Automated HPOMDP Construction through Data-mining Techniques in the Intelligent Environment Domain.
Youngblood, G. M., Heierman, E. O., Cook, D. J., & Holder, L. B. (2005) · Cited by 3
Multimodal estimation of user interruptibility for smart mobile telephones
Malkin, R. E., Chen, D., Yang, J., & Waibel, A. (2006) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/1180995.1181018 · Cited by 3
Hierarchical HMM-based semantic concept labeling model
Mengistu, K. T., Hannemann, M., Baum, T., & Wendemuth, A. (2008) · IEEE Spoken Language Technology Workshop (SLT) · doi:10.1109/slt.2008.4777839 · Cited by 3
Study on Product Named Entity Recognition for Business Information Extraction
Fei-fan, L., & Fujitsu, R. (2006) · Zhongwen xinxi xuebao · Cited by 3
Probabilistic graphical models and algorithms for genomic analysis
Xing, P., Karp, R. M., Jordan, M. I., & Russell, S. (2004) · Cited by 3
Jabbari, F., Sameti, H., & Bokaei, M. H. (2011) · International Conference on Natural Language Processing and Knowledge Engineering (NLPKE) · doi:10.1109/nlpke.2011.6138187 · Cited by 3
Graphical Models for Planning and Imitation in Uncertain Environments
Verma, D. K., & Rao, R. P. N. (2008) · Cited by 3
Modeling Multiple-Mode Systems with Predictive State Representations
Wolfe, B., James, M. R., & Singh, S. P. (2010) · Opus: Research & Creativity (Indiana University – Purdue University Fort Wayne) · Cited by 3
Probabilistic Model for Structured Document Mapping
Wisniewski, G., Maes, F., Denoyer, L., & Gallinari, P. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-73499-4_64 · Cited by 3
Learning Hidden Markov Models with Hidden Markov Trees as Observation Distributions
Milone, D. H., & Persia, L. E. D. (2008) · INTELIGENCIA ARTIFICIAL · doi:10.4114/ia.v12i37.953 · Cited by 3
Video content categorization using the double decomposition
Du, Y., Chen, F., Xu, W., & Qian, X. (2012) · Multimedia Tools and Applications · doi:10.1007/s11042-012-1213-y · Cited by 3
Discrete dynamic system oriented on the formation of prebiotic dipeptides from Rode's experiment.
Polanco, C., Samaniego, J. L., Buhse, T., & González, J. A. C. (2014) · Acta Biochimica Polonica · doi:10.18388/abp.2014_1836 · Cited by 3
Hierarchical Fuzzy Hidden Markov Chain for Web Applications
Sujatha, R., & Rajalaxmi, T. M. (2015) · International Journal of Information Technology & Decision Making · doi:10.1142/s0219622015500376 · Cited by 3
Bottom-up learning of hierarchical models in a class of deterministic POMDP environments
Itoh, H., Fukumoto, H., Wakuya, H., & Furukawa, T. (2015) · International Journal of Applied Mathematics and Computer Science · doi:10.1515/amcs-2015-0044 · Cited by 3
Recognition of Human Behavior Patterns Using Depth Information and Gaussian Feature Maps.
Spehr, J., Islami, M. T. F. A., Winkelbach, S., & Wahl, F. M. (2012) · Cited by 3
Hierarchical HMM-based Failure Isolation for Cognitive Robots
Altan, D., & Sarıel, S. (2014) · International Conference on Agents and Artificial Intelligence (ICAART) · doi:10.5220/0004921502990304 · Cited by 3
Omega: An Architecture for AI Unification
Özkural, E. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-52152-3_28 · Cited by 3
Kim, M., Han, A.-L., Kim, T.-Y., & Lim, J. (2020) · Applied Sciences · doi:10.3390/app10186494 · Cited by 3
activIn: A novel Non-Intrusive Activity Inference Tool
Dimara, A., Krinidis, S., & Tzovaras, D. K. (2020) · IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) · doi:10.1109/ithings-greencom-cpscom-smartdata-cybermatics50389.2020.00073 · Cited by 3
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
