메뉴 건너뛰기




Volumn 1299, Issue , 1997, Pages 171-191

Machine learning for information extraction

Author keywords

[No Author keywords available]

Indexed keywords

MACHINE LEARNING;

EID: 84947783833     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-63438-x_9     Document Type: Conference Paper
Times cited : (2)

References (95)
  • 6
    • 84947705226 scopus 로고    scopus 로고
    • In L. Kaelbling, editor, Machine Learning (Special Issue on Reinforcement Learning), volume 22. 1996.
    • Kaelbling, L.1
  • 9
    • 84947768319 scopus 로고    scopus 로고
    • N. Abe and H. Li. Learning word association norms using tree cut pair models. In Proceedings of the 13th Conference on Machine Learning, pages 3-11, Bari, Italy, 1996. Morgan Kaufman.
    • Abe, N.1    Li, H.2
  • 11
    • 85151728371 scopus 로고
    • Residual algorithms: Reiforcement learning with function approximation
    • Lake Tahoe, CA
    • L. Baird. Residual algorithms: Reiforcement learning with function approximation. In 12th International Conference on Machine Learning, pages 30-37, Lake Tahoe, CA, 1995.
    • (1995) 12Th International Conference on Machine Learning , pp. 30-37
    • Baird, L.1
  • 13
    • 0016521417 scopus 로고
    • Toward a mathematical theory of inductive inference
    • M. Blum and L. Blum. Toward a mathematical theory of inductive inference. Information and Control, 28:125-155, 1975.
    • (1975) Information and Control , vol.28 , pp. 125-155
    • Blum, M.1    Blum, L.2
  • 15
    • 0030196364 scopus 로고    scopus 로고
    • Stacked regression
    • L. Breiman. Stacked regression. Machine Learning, 24(l):49-64, 1996.
    • (1996) Machine Learning , vol.24 , Issue.1 , pp. 49-64
    • Breiman, L.1
  • 16
    • 0000972865 scopus 로고
    • Learning by analogy: Formulating and generalizing plans from past experience
    • J.G. Carbonell, R.S. Michalski, and T. Mitchell, editors, Morgan Kaufmann
    • J.G. Carbonell. Learning by analogy: formulating and generalizing plans from past experience. In J.G. Carbonell, R.S. Michalski, and T. Mitchell, editors, Machine Learning, an Artificial Intelligence Approach, pages 137-161. Morgan Kaufmann, 1983.
    • (1983) Machine Learning, an Artificial Intelligence Approach , pp. 137-161
    • Carbonell, J.G.1
  • 17
    • 84947768958 scopus 로고
    • Inductive logic 1945-1977
    • E. Agazzi, editor, D. Reidel Publ. Co
    • L. J. Cohen. Inductive logic 1945-1977. In E. Agazzi, editor, Modern Logic. D. Reidel Publ. Co., 1980.
    • (1980) Modern Logic
    • Cohen, L.J.1
  • 18
    • 84927764933 scopus 로고
    • Text categorization and relational learning
    • Lake Tahoe, CA
    • W. Cohen. Text categorization and relational learning. In 12th International Conference on Machine Learning, pages 124-132, Lake Tahoe, CA, 1995.
    • (1995) 12Th International Conference on Machine Learning , pp. 124-132
    • Cohen, W.1
  • 19
    • 0028405380 scopus 로고
    • Incremental abductive explanation based learning
    • W. W. Cohen. Incremental abductive explanation based learning. Machine Learning, 15:5-24, 1993.
    • (1993) Machine Learning , vol.15 , pp. 5-24
    • Cohen, W.W.1
  • 20
  • 22
    • 0027696338 scopus 로고
    • Using genetic algorithms for concept learning
    • K. A. De Jong, W. M. Spears, and F. D. Gordon. Using genetic algorithms for concept learning. Machine Learning, 13:161-188, 1993.
    • (1993) Machine Learning , vol.13 , pp. 161-188
    • De Jong, K.A.1    Spears, W.M.2    Gordon, F.D.3
  • 23
    • 0000017646 scopus 로고
    • Explanation based generalization: An alternative view
    • G. F. DeJong and R. J. Mooney. Explanation based generalization: an alternative view. Machine Learning, 1:145-176, 1986.
    • (1986) Machine Learning , vol.1 , pp. 145-176
    • Dejong, G.F.1    Mooney, R.J.2
  • 24
    • 0020098693 scopus 로고
    • Any discrimination rule can have an arbitrarily bad probability of error for finite sample size
    • PAMI-2
    • L. Devroye. Any discrimination rule can have an arbitrarily bad probability of error for finite sample size. IEEE Transaction on Pattern Analysis and Machine Intelligence, PAMI-2:154-157, 1982.
    • (1982) IEEE Transaction on Pattern Analysis and Machine Intelligence , pp. 154-157
    • Devroye, L.1
  • 25
    • 84947808753 scopus 로고    scopus 로고
    • R. Feldman and I. Dagan. Knowledge discovery in textual databases (kdt). In Proceedings of the First International Conference on Knowlede Discovery and Data Mining, pages 112-117, Montreal, Quebec, 1995. AAAI Press.
    • Feldman, R.1    Dagan, I.2
  • 27
    • 0343442766 scopus 로고
    • Knowledge acquisition via incremental conceptual clustering
    • D. H. Fisher. Knowledge acquisition via incremental conceptual clustering. Machine Learning, 2:139-172, 1987.
    • (1987) Machine Learning , vol.2 , pp. 139-172
    • Fisher, D.H.1
  • 28
    • 84983110889 scopus 로고
    • A decision-theorethic generalization of on-line learning and an application to boosting
    • Springer-Verlag
    • Y. Freund and R. E. Schapire. A decision-theorethic generalization of on-line learning and an application to boosting. In Second European Conference on Computational Learning Theory, pages 23-37. Springer-Verlag, 1995.
    • (1995) Second European Conference on Computational Learning Theory , pp. 23-37
    • Freund, Y.1    Schapire, R.E.2
  • 30
    • 0000662737 scopus 로고
    • Search-intensive concept induction
    • A. Giordana and F. Neri. Search-intensive concept induction. Evolutionary Computation, 3 (4):375-416, 1995.
    • (1995) Evolutionary Computation , vol.3 , Issue.4 , pp. 375-416
    • Giordana, A.1    Neri, F.2
  • 31
    • 0031164490 scopus 로고    scopus 로고
    • Integrating multiple learning strategies in first order logics
    • To appear
    • A. Giordana, F. Neri, L. Saitta, and M. Botta. Integrating multiple learning strategies in first order logics. Machine Learning, To appear, 1997.
    • (1997) Machine Learning
    • Giordana, A.1    Neri, F.2    Saitta, L.3    Botta, M.4
  • 33
    • 49949150022 scopus 로고
    • Language identification in the limit
    • E. M. Gold. Language identification in the limit. Information and Control, 10:447-474, 1967.
    • (1967) Information and Control , vol.10 , pp. 447-474
    • Gold, E.M.1
  • 34
    • 0027696043 scopus 로고
    • Competition-based induction of decision models from examples
    • D. P. Greene and S. F. Smith. Competition-based induction of decision models from examples. Machine Learning, 13:229-258, 1993.
    • (1993) Machine Learning , vol.13 , pp. 229-258
    • Greene, D.P.1    Smith, S.F.2
  • 35
    • 0024082469 scopus 로고
    • Quantifying inductive bias - Ai learning algorithms and valiant’s learning framework
    • D. Haussler. Quantifying inductive bias - ai learning algorithms and valiant’s learning framework. Artificial Intelligence, 36:177-221, 1988.
    • (1988) Artificial Intelligence , vol.36 , pp. 177-221
    • Haussler, D.1
  • 36
    • 2542455833 scopus 로고
    • Learning conjunctive concepts in structural domains
    • D. Haussler. Learning conjunctive concepts in structural domains. Machine Learning, 4:7-40, 1989.
    • (1989) Machine Learning , vol.4 , pp. 7-40
    • Haussler, D.1
  • 38
    • 0024880831 scopus 로고
    • Multilayer feed-forward networks are universal approximators
    • K. Hornik, M. Stinchcombe, and H. White. Multilayer feed-forward networks are universal approximators. Neural Networks, 2:359-366, 1989.
    • (1989) Neural Networks , vol.2 , pp. 359-366
    • Hornik, K.1    Stinchcombe, M.2    White, H.3
  • 39
    • 0027696178 scopus 로고
    • A knowledge intensive genetic algorithm for supervised learning
    • C.Z. Janikow. A knowledge intensive genetic algorithm for supervised learning. Machine Learning, 13:198-228, 1993.
    • (1993) Machine Learning , vol.13 , pp. 198-228
    • Janikow, C.Z.1
  • 41
    • 0016939124 scopus 로고
    • Continuous speech recognition by statistical methods
    • F. Jelinek. Continuous speech recognition by statistical methods. In Proceedings of IEEE, volume 64, pages 532-556, 1976.
    • (1976) Proceedings of IEEE , vol.64 , pp. 532-556
    • Jelinek, F.1
  • 42
    • 84947760804 scopus 로고    scopus 로고
    • R. Kohavi. A study of cross-validation and bootstrap for accuracy estimation and model selection. In Proceedings of the 14th International Joint Conference on Artificial intelligence, pages 1137-1143, Montreal, Quebec, 1995. A A AI Press.
    • Kohavi, R.1
  • 46
    • 9744236956 scopus 로고
    • Editorial: On machine learning
    • P. Langley. Editorial: On machine learning. Machine Learning, 1:5-10, 1986.
    • (1986) Machine Learning , vol.1 , pp. 5-10
    • Langley, P.1
  • 47
    • 1642452775 scopus 로고
    • Editorial: Machine learning as an experimental science
    • P. Langley. Editorial: Machine learning as an experimental science. Machine Learning, 3:5-8, 1988.
    • (1988) Machine Learning , vol.3 , pp. 5-8
    • Langley, P.1
  • 51
    • 0020717606 scopus 로고
    • EURISKO: A program that learns new heuristics and domain concepts, the nature of heuristics iii: Program design and results
    • D. B. Lenat. EURISKO: A program that learns new heuristics and domain concepts, the nature of heuristics iii: Program design and results. Artificial Intelligence, 21, 1983.
    • (1983) Artificial Intelligence , vol.21
    • Lenat, D.B.1
  • 52
  • 53
    • 0028461596 scopus 로고
    • Text categorization for multiple users based on semantic feature from a machine readable dictionary
    • E. D. Liddy, W. Paik, and E. S. Yu. Text categorization for multiple users based on semantic feature from a machine readable dictionary. ACM Transaction on Information Systems, 12:278-295, 1994.
    • (1994) ACM Transaction on Information Systems , vol.12 , pp. 278-295
    • Liddy, E.D.1    Paik, W.2    Yu, E.S.3
  • 54
    • 0027719422 scopus 로고
    • Answering the connessionistic challenge: A symbolic model of learning the past tenses of english verbs
    • C. X. Ling and M. Marinov. Answering the connessionistic challenge: a symbolic model of learning the past tenses of english verbs. Cognition, 49:235-290, 1993.
    • (1993) Cognition , vol.49 , pp. 235-290
    • Ling, C.X.1    Marinov, M.2
  • 56
    • 0003046840 scopus 로고
    • A theory and methodology of inductive learning
    • In R. Michalski, J. Carbonell, and T. Mitchell, editors, Morgan Kaufmann, Los Altos, CA
    • R.S. Michalski. A theory and methodology of inductive learning. In R. Michalski, J. Carbonell, and T. Mitchell, editors, Machine Learning, an Artificial Intelligence Approach, volume I, pages 83-134. Morgan Kaufmann, Los Altos, CA, 1983.
    • (1983) Machine Learning, an Artificial Intelligence Approach , vol.1 , pp. 83-134
    • Michalski, R.S.1
  • 57
    • 0003046842 scopus 로고
    • Learning from observation: Conceptual clustering
    • In R. Michalski, J. Carbonell, and T. Mitchell, editors, Morgan Kaufmann, Los Altos, CA
    • R.S. Michalski and R. Stepp. Learning from observation: conceptual clustering. In R. Michalski, J. Carbonell, and T. Mitchell, editors, Machine Learning, an Artificial Intelligence Approach, volume I, pages 83-134. Morgan Kaufmann, Los Altos, CA, 1983.
    • (1983) Machine Learning, an Artificial Intelligence Approach , vol.1 , pp. 83-134
    • Michalski, R.S.1    Stepp, R.2
  • 62
    • 84947780095 scopus 로고    scopus 로고
    • Webwatcher: A learning apprentice for the world wide web
    • Stanford, CA
    • T.M. Mitchell. Webwatcher: a learning apprentice for the world wide web. In AAAI Spring Symposium, Stanford, CA, 1995.
    • AAAI Spring Symposium , pp. 1995
    • Mitchell, T.M.1
  • 65
    • 84947765252 scopus 로고    scopus 로고
    • F. Neri. lint Order Logic Concept Learning by means of a Distributed Genetic Algorithm. PhD thesis, University of Torino, Italy, 1997. Available at http://www.di.unito.it/~neri/phd/thesis.ps.gz.
    • Neri, F.1
  • 70
    • 8844261754 scopus 로고
    • The utility of knowledge in inductive learning
    • M.J. Pazzani and D. Kibler. The utility of knowledge in inductive learning. Machine Learning, 14:57-94, 1992.
    • (1992) Machine Learning , vol.14 , pp. 57-94
    • Pazzani, M.J.1    Kibler, D.2
  • 71
    • 0001172265 scopus 로고
    • Learning logical definitions from relations
    • J. R. Quinlan. Learning logical definitions from relations. Machine Learning, 5:239-266, 1990.
    • (1990) Machine Learning , vol.5 , pp. 239-266
    • Quinlan, J.R.1
  • 72
    • 0028460389 scopus 로고
    • Information extraction as a basis for high precision text classification
    • E. Riloff and W. Lehnert. Information extraction as a basis for high precision text classification. ACM Transaction on Information Systems, 12:296-333, 1994.
    • (1994) ACM Transaction on Information Systems , vol.12 , pp. 296-333
    • Riloff, E.1    Lehnert, W.2
  • 73
    • 0021466584 scopus 로고
    • Universal coding, information, prediction, and estimation
    • J. Rissanen. Universal coding, information, prediction, and estimation. IEEE Transaction on Information Theory, IT-30:629-636, 1984.
    • (1984) IEEE Transaction on Information Theory , vol.30 , pp. 629-636
    • Rissanen, J.1
  • 74
    • 0002284631 scopus 로고
    • Principles of categorization
    • In E. W. Rosch and B. Lloyd, editors, Earlbaum, Hillsdale, NJ
    • E. W. Rosch. Principles of categorization. In E. W. Rosch and B. Lloyd, editors, Cognition and Categorization. Earlbaum, Hillsdale, NJ, 1978.
    • (1978) Cognition and Categorization
    • Rosch, E.W.1
  • 75
    • 11144273669 scopus 로고
    • The perceptron: A probabilistic model for information storage and organization in the brain
    • F. Rosenblatt. The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 65:386-407, 1958.
    • (1958) Psychological Review , vol.65 , pp. 386-407
    • Rosenblatt, F.1
  • 77
    • 84947805086 scopus 로고    scopus 로고
    • M. Sahami, M. Hearst, and E. Saund. Applying the multiple cause mixture model to text categorization. In Proceedings of the 13th Conference on Machine Learning, pages 435-443, Bari, Italy, 1996. Morgan Kaufman.
    • Sahami, M.1    Hearst, M.2    Saund, E.3
  • 79
    • 0027599246 scopus 로고
    • Multistrategy learning and theory revision
    • L. Saitta, M. Botta, and F. Neri. Multistrategy learning and theory revision. Machine Learning, 11:153-172, 1993.
    • (1993) Machine Learning , vol.11 , pp. 153-172
    • Saitta, L.1    Botta, M.2    Neri, F.3
  • 80
    • 0000417994 scopus 로고
    • Development in automatic text retrieval
    • G. Salton. Development in automatic text retrieval. Science, 253:974-980, 1991.
    • (1991) Science , vol.253 , pp. 974-980
    • Salton, G.1
  • 81
    • 84983750945 scopus 로고
    • Schaffen A conservation law for generalization performance
    • C, New Brunswick, NJ
    • C. Schaffen A conservation law for generalization performance. In 11th International Conference on Machine Learning, pages 259-265, New Brunswick, NJ, 1994.
    • (1994) 11Th International Conference on Machine Learning , pp. 259-265
  • 82
    • 0025448521 scopus 로고
    • The strenght of weak learnability
    • R. E. Schapire. The strenght of weak learnability. Machine Learning, 5:197-227, 1990.
    • (1990) Machine Learning , vol.5 , pp. 197-227
    • Schapire, R.E.1
  • 83
    • 0028463710 scopus 로고
    • Modeling cognitive development on balance scale phenomena
    • T. R. Shultz, D. Mareschal, and W. C. Schmidt. Modeling cognitive development on balance scale phenomena. Machine Learning, 16:57-86, 1994.
    • (1994) Machine Learning , vol.16 , pp. 57-86
    • Shultz, T.R.1    Mareschal, D.2    Schmidt, W.C.3
  • 84
    • 50549204079 scopus 로고
    • A formal theory of inductive inference
    • R. J. Solomonoff. A formal theory of inductive inference. Information and Control, 7:1-22, 224-254, 1964.
    • (1964) Information and Control , vol.7 , pp. 224-254
    • Solomonoff, R.J.1
  • 85
    • 0029308343 scopus 로고
    • Comprehension grammars generated from ml on nl sentences
    • P. Suppes, M. Bottner, and L. Liang. Comprehension grammars generated from ml on nl sentences. Machine Learning, 19:133-152, 1990.
    • (1990) Machine Learning , vol.19 , pp. 133-152
    • Suppes, P.1    Bottner, M.2    Liang, L.3
  • 86
    • 33847202724 scopus 로고
    • Learning to predict by the methods of temporal differences
    • R.S. Sutton. Learning to predict by the methods of temporal differences. Machine Learning, 3:9-44, 1988.
    • (1988) Machine Learning , vol.3 , pp. 9-44
    • Sutton, R.S.1
  • 87
    • 84947791097 scopus 로고    scopus 로고
    • D. Thau. Primacy effects and selective attention in incremental clustering. In Fourteenth Annual Conference of the Cognitive Science Society, pages 219-223, Hillsdale, NJ, 1992. Lawrence Erlbaum Associates.
    • Thau, D.1
  • 89
    • 0021518106 scopus 로고
    • Learning fallible deterministic finite automata
    • L. G. Valiant. Learning fallible deterministic finite automata. Communications of the ACM, 27:1134-1142, 1984.
    • (1984) Communications of the ACM , vol.27 , pp. 1134-1142
    • Valiant, L.G.1
  • 90
  • 91
    • 0001954668 scopus 로고
    • Necessary and sufficient conditions for the uniform convergence of means to their expectations
    • V. N. Vapnik and Y. A. Chervonenkis. Necessary and sufficient conditions for the uniform convergence of means to their expectations. Theory Probability Applications, 26:532-553, 1981.
    • (1981) Theory Probability Applications , vol.26 , pp. 532-553
    • Vapnik, V.N.1    Chervonenkis, Y.A.2
  • 93
    • 34147137700 scopus 로고
    • Mental models of the earth: A study of conceptual change in childhood
    • S. Vosniadou and W. F. Brewer. Mental models of the earth: A study of conceptual change in childhood. Cognitive Psychology, 24:535-585, 1992.
    • (1992) Cognitive Psychology , vol.24 , pp. 535-585
    • Vosniadou, S.1    Brewer, W.F.2
  • 95
    • 84947804613 scopus 로고    scopus 로고
    • O. R. Zaane and J. Han. Resource and knowledge discovery in global information systems: A preliminary design and experiment. In Proceedings of the First International Conference on Knowlede Discovery and Data Mining, pages 331-336, Menlo Park, CA, 1995. AAAI Press.
    • Zaane, O.R.1    Han, J.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.