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Volumn , Issue , 2007, Pages 132-140

Active EM to reduce noise in activity recognition

Author keywords

Active learning; Expectation maximization; Intelligent interface; Machine learning; Noise

Indexed keywords

ALGORITHMS; ELECTROMAGNETISM; INTELLIGENT SYSTEMS; LEARNING SYSTEMS; NOISE ABATEMENT; PROJECT MANAGEMENT; QUERY PROCESSING; ROBUST CONTROL;

EID: 34648823321     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1216295.1216323     Document Type: Conference Paper
Times cited : (3)

References (29)
  • 1
    • 0000710299 scopus 로고
    • Queries and concept learning
    • April
    • D. Angluin. Queries and concept learning. Machine Learning, 2(4):319-342, April 1988.
    • (1988) Machine Learning , vol.2 , Issue.4 , pp. 319-342
    • Angluin, D.1
  • 2
    • 0000492326 scopus 로고
    • Learning from noisy examples
    • D. Angluin and P. Laird. Learning from noisy examples. Machine Learning, 2(4):343-370, 1988.
    • (1988) Machine Learning , vol.2 , Issue.4 , pp. 343-370
    • Angluin, D.1    Laird, P.2
  • 3
    • 24144453496 scopus 로고    scopus 로고
    • Noise-tolerant learning, the parity problem, and the statistical query model
    • A. Blum, A. Kalai, and H. Wasserman. Noise-tolerant learning, the parity problem, and the statistical query model. Journal of the ACM, pages 506-519, 2003.
    • (2003) Journal of the ACM , pp. 506-519
    • Blum, A.1    Kalai, A.2    Wasserman, H.3
  • 4
    • 0031620208 scopus 로고    scopus 로고
    • Combining labeled and unlabeled data with co-training
    • A. Blum and T. Mitchell. Combining labeled and unlabeled data with co-training. In Proc. of COLT-98, pages 92-100, 1998.
    • (1998) Proc. of COLT-98 , pp. 92-100
    • Blum, A.1    Mitchell, T.2
  • 5
    • 0000046054 scopus 로고    scopus 로고
    • Identifying mislabeled training data
    • C. E. Brodley and M. Friedl. Identifying mislabeled training data. JAIR, 11:131-167, 1999.
    • (1999) JAIR , vol.11 , pp. 131-167
    • Brodley, C.E.1    Friedl, M.2
  • 6
    • 0028424239 scopus 로고
    • Improving generalization with active learning
    • D. Cohn, L. Atlas, and R. Ladner. Improving generalization with active learning. Machine Learning, 15(2):201-221, 1994.
    • (1994) Machine Learning , vol.15 , Issue.2 , pp. 201-221
    • Cohn, D.1    Atlas, L.2    Ladner, R.3
  • 9
    • 33746043801 scopus 로고    scopus 로고
    • Automatically classifying emails into activities
    • M. Dredze, T. Lau, and N. Kushmerick. Automatically classifying emails into activities. In Proc. of IUI-06, pages 70-77, 2006.
    • (2006) Proc. of IUI-06 , pp. 70-77
    • Dredze, M.1    Lau, T.2    Kushmerick, N.3
  • 10
    • 0031209604 scopus 로고    scopus 로고
    • Selective sampling using the query by committee algorithm
    • Y. Freund, H. Seung, E. Shamir, and N. Tishby. Selective sampling using the query by committee algorithm. Machine Learning, 28:133-168, 1997.
    • (1997) Machine Learning , vol.28 , pp. 133-168
    • Freund, Y.1    Seung, H.2    Shamir, E.3    Tishby, N.4
  • 11
    • 0001670678 scopus 로고    scopus 로고
    • The lumiere project: Bayesian user modeling for inferring the goals and needs of software users
    • E. Horvitz, J. Breese, D. Heckerman, D. Hovel, and K. Rommelse. The lumiere project: Bayesian user modeling for inferring the goals and needs of software users. In UAI-98, pages 256-265, 1998.
    • (1998) UAI-98 , pp. 256-265
    • Horvitz, E.1    Breese, J.2    Heckerman, D.3    Hovel, D.4    Rommelse, K.5
  • 12
    • 0002561422 scopus 로고    scopus 로고
    • Attention-sensitive alerting
    • E. Horvitz, A. Jacobs, and D. Hovel. Attention-sensitive alerting. In UAI-99, pages 305-313, 1999.
    • (1999) UAI-99 , pp. 305-313
    • Horvitz, E.1    Jacobs, A.2    Hovel, D.3
  • 13
    • 18844386512 scopus 로고    scopus 로고
    • Learning and reasoning about interruption
    • E. Horvitz, A. Jacobs, and D. Hovel. Learning and reasoning about interruption. In Proc. of ICMI-03, pages 20-27, 2003.
    • (2003) Proc. of ICMI-03 , pp. 20-27
    • Horvitz, E.1    Jacobs, A.2    Hovel, D.3
  • 14
    • 0001938951 scopus 로고    scopus 로고
    • Transductive inference for text classification using support vector machines
    • T. Joachims. Transductive inference for text classification using support vector machines. In Proc. of ICML-99, pages 200-209, 1999.
    • (1999) Proc. of ICML-99 , pp. 200-209
    • Joachims, T.1
  • 15
    • 0038375550 scopus 로고    scopus 로고
    • UMEA: Translating interaction histories into project contexts
    • V. Kaptelinin. UMEA: translating interaction histories into project contexts. In SIGCHI, pages 353-360, 2003.
    • (2003) SIGCHI , pp. 353-360
    • Kaptelinin, V.1
  • 16
    • 0032202014 scopus 로고    scopus 로고
    • Efficient noise-tolerant learning from statistical queries
    • M. Kearns. Efficient noise-tolerant learning from statistical queries. Journal of the ACM, pages 983-1006, 1998.
    • (1998) Journal of the ACM , pp. 983-1006
    • Kearns, M.1
  • 17
    • 33644608450 scopus 로고    scopus 로고
    • Automated email activity management: An unsupervised learning approach
    • N. Kushmerick and T. Lau. Automated email activity management: an unsupervised learning approach. In Proc. of IUI-05, pages 67-74, 2005.
    • (2005) Proc. of IUI-05 , pp. 67-74
    • Kushmerick, N.1    Lau, T.2
  • 18
    • 85013879626 scopus 로고
    • A sequential algorithm for training text classifiers
    • D. Lewis and W. Gale. A sequential algorithm for training text classifiers. In Proc. of SIGIR-94, pages 3-12, 1994.
    • (1994) Proc. of SIGIR-94 , pp. 3-12
    • Lewis, D.1    Gale, W.2
  • 19
    • 0000314722 scopus 로고    scopus 로고
    • Employing EM in poolbased active learning for text classification
    • A. McCallum and K. Nigam. Employing EM in poolbased active learning for text classification. In Proc.of ICML-98, pages 350-358, 1998.
    • (1998) Proc.of ICML-98 , pp. 350-358
    • McCallum, A.1    Nigam, K.2
  • 20
    • 34648878629 scopus 로고    scopus 로고
    • Extracting knowledge about users' activities from raw workstation contents
    • T. M. Mitchell, S. H. Wang, Y. Huang, and A. Cheyer. Extracting knowledge about users' activities from raw workstation contents. In Proc.of AAAI-06, 2006.
    • (2006) Proc.of AAAI-06
    • Mitchell, T.M.1    Wang, S.H.2    Huang, Y.3    Cheyer, A.4
  • 21
    • 3242788638 scopus 로고    scopus 로고
    • Active + semisupervised learning = robust multi-view learning
    • I. Muslea, S. Minton, and C. Knoblock. Active + semisupervised learning = robust multi-view learning. In Proc.of ICML-02, pages 435-442, 2002.
    • (2002) Proc.of ICML-02 , pp. 435-442
    • Muslea, I.1    Minton, S.2    Knoblock, C.3
  • 22
    • 85136905861 scopus 로고    scopus 로고
    • Analyzing the effectiveness and applicability of co-training
    • K. Nigam and R. Ghani. Analyzing the effectiveness and applicability of co-training. In Proc. of CIKM-00, pages 86-93, 2000.
    • (2000) Proc. of CIKM-00 , pp. 86-93
    • Nigam, K.1    Ghani, R.2
  • 23
    • 0033886806 scopus 로고    scopus 로고
    • Text classification from labeled and unlabeled documents using EM
    • K. Nigam, A. McCallum, S. Thrun, and T. Mitchell. Text classification from labeled and unlabeled documents using EM. Machine Learning, 39(2/3): 103-134, 2000.
    • (2000) Machine Learning , vol.39 , Issue.2-3 , pp. 103-134
    • Nigam, K.1    McCallum, A.2    Thrun, S.3    Mitchell, T.4
  • 24
    • 19944409414 scopus 로고    scopus 로고
    • The probabilistic activity toolkit: Towards enabling activity-aware computer interfaces
    • Technical Report IRS-TR-03-013, Intel Research Lab, Seattle, WA
    • M. Philipose, K. Fishkin, M. Perkowitz, D. Patterson, and D. Hahnel. The probabilistic activity toolkit: Towards enabling activity-aware computer interfaces. Technical Report IRS-TR-03-013, Intel Research Lab, Seattle, WA, 2003.
    • (2003)
    • Philipose, M.1    Fishkin, K.2    Perkowitz, M.3    Patterson, D.4    Hahnel, D.5
  • 25
    • 2142727946 scopus 로고    scopus 로고
    • Limitations of co-training for natural language learning from large datasets
    • D. Pierce and C. Cardie. Limitations of co-training for natural language learning from large datasets. In Proc. of EMNLP, pages 1-9, 2001.
    • (2001) Proc. of EMNLP , pp. 1-9
    • Pierce, D.1    Cardie, C.2
  • 26
    • 84948481845 scopus 로고
    • An algorithm for suffix stripping
    • M. Porter. An algorithm for suffix stripping. Program, 14(3):130-137, 1980.
    • (1980) Program , vol.14 , Issue.3 , pp. 130-137
    • Porter, M.1
  • 27
    • 1242285091 scopus 로고
    • Active sampling for class probability estimation and ranking
    • M. Saar-Tsechansky and F. Provost. Active sampling for class probability estimation and ranking. Machine Learning, 54(2):153-178, 1994.
    • (1994) Machine Learning , vol.54 , Issue.2 , pp. 153-178
    • Saar-Tsechansky, M.1    Provost, F.2
  • 28
    • 33746089186 scopus 로고    scopus 로고
    • A hybrid learning system for recognizing user tasks from desktop activities and email messages
    • J. Shen, L. Li, T. Dietterich, and J. Herlocker. A hybrid learning system for recognizing user tasks from desktop activities and email messages. In Proc. of IUI-06, pages 86-92, 2006.
    • (2006) Proc. of IUI-06 , pp. 86-92
    • Shen, J.1    Li, L.2    Dietterich, T.3    Herlocker, J.4
  • 29
    • 0003007938 scopus 로고    scopus 로고
    • Support vector machine active learning with applications to text classification
    • S. Tong and D. Koller. Support vector machine active learning with applications to text classification. In Proc. of ICML-00, pages 999-1006, 2000.
    • (2000) Proc. of ICML-00 , pp. 999-1006
    • Tong, S.1    Koller, D.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.