HMMS for both labeled and unlabeled time series data

Neural Networks for Signal Processing - Proceedings of the IEEE Workshop

Volumn , Issue , 2001, Pages 93-102

  Inoue, Masashi ^a   Ueda, Naonori ^b  

^a NARA INSTITUTE OF SCIENCE AND TECHNOLOGY   (Japan)

^b NTT Corporation   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTER SIMULATION; DATA REDUCTION; MATHEMATICAL MODELS; PROBABILITY DENSITY FUNCTION; TIME SERIES ANALYSIS;

UNLABELED TIME SERIES DATA;

MARKOV PROCESSES;

EID: 0035783823     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (5)

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2. T. Joachims, "Transductive Inference for Text Classification using Support Vector Machines," in International Conference on Machine Learning (ICML), 1999, pp. 200-209.
3. D.J. Miller and H.S. Uyar, "A Mixture of Experts Classifier with Learning Based on Both Labelled and Unlabelled Data," in Advances in Neural Information Processing Systems 9, 1997, pp. 571-577.
4. K. Nigam, A.K. Mccallum, S. Thrun and T. Mitchell, "Text Classification from Labeled and Unlabeled Documents using EM," Machine Learning, vol. 39, pp. 103-134, 2000.
5. B. Shahshahani and D. Landgrebe, "The effect of unlabeled samples in reducing the small sample size problem and mitigating the Hughes phenomenon," IEEE Transactions on Geoscience and Remote Sensing, vol. 32, no. 5, pp. 1087-1095, 1994.