Referências Bibliográficas
[Alu03] ALUÍSIO, S. M.; PELIZZONI, J. M.; MARCHI, A. R.; DE OLIVEIRA, L.;
MANENTI, R. ; MARQUIAFÁVEL, V.. An account of the challenge of
tagging a reference corpus for brazilian portuguese. In: PROPOR,
p. 110–117, 2003. 7.3
[Ben02] BENNETT, K. P.; DEMIRIZ, A. ; MACLIN, R.. Exploiting unlabe-
PUC-Rio - Certificação Digital Nº 0510956/CA
led data in ensemble methods. In: PROCEEDINGS OF THE EIGHTH
ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, p. 289–296, New York, NY, USA, 2002. 8
[Bha07] BHARATI, A.; MANNEM, P. R.. Introduction to shallow parsing
contest on south asian languages. In: PROCEEDINGS OF THE IJCAI
AND THE WORKSHOP ON SHALLOW PARSING FOR SOUTH ASIAN
LANGUAGES (SPSAL), p. 1–8, 2007. 7.4
[Blu98] BLUM, A.; MITCHELL, T.. Combining labeled and unlabeled
data with co-training. In: COLT: PROCEEDINGS OF THE WORKSHOP
ON COMPUTATIONAL LEARNING THEORY, MORGAN KAUFMANN PUBLISHERS, 1998. 4.1.3
[Bra02] BRANTS, S.; DIPPER, S.; HANSEN, S.; LEZIUS, W. ; SMITH, G..
The TIGER treebank. In: PROCEEDINGS OF THE WORKSHOP ON
TREEBANKS AND LINGUISTIC THEORIES, Sozopol, 2002. 7.3
[Bre96] BREIMAN, L.. Bagging predictors. Mach. Learn., 24(2):123–140,
1996. 1.1
[Bri92] BRILL, E.. A simple rule-based part of speech tagger. In:
PROCEEDINGS OF THE THIRD CONFERENCE ON APPLIED NATURAL
LANGUAGE PROCESS, Trento, Italy, 1992. Association for Computational
Linguistics. 5.2
[Bri94] BRILL, E.; RESNIK, P.. A rule-based approach to prepositional phrase attachment disambiguation. In: PROCEEDINGS OF COLING’94, Kyoto, Japan, 1994.
Referências Bibliográficas
82
[Bri95] BRILL, E.. Transformation-based error-driven learning and
natural language processing: A case study in part-of-speech
tagging. Computational Linguistics, 21(4):543–565, 1995. 7.6.1
[Car03] NG, V.; CARDIE, C.. Weakly supervised natural language learning without redundant views. In: PROCEEDINGS OF THE 2003
CONFERENCE OF THE NORTH AMERICAN CHAPTER OF THE ACL ON
HUMAN LANGUAGE TECHNOLOGY, p. 94–101, Morristown, NJ, USA,
2003. 4.1.3
[Coh08] COHEN, B. K.; HUNTER, L.. Getting started in text mining.
PLoS Computational Biology, 4(1):e20+, January 2008. 1.1
[Dem02] DEMIRIZ, A.; BENNETT, K. P. ; SHAWE-TAYLOR, J.. Linear
programming boosting via column generation. Machine Learning,
PUC-Rio - Certificação Digital Nº 0510956/CA
46(1-3):225–254, 2002. 2.3
[Dru93] DRUCKER, H.; SCHAPIRE, R. E. ; SIMARD, P.. Boosting performance in neural networks. International Journal of Pattern Recognition
and Artificial Intelligence, 7(4):705–719, 1993. 1.1, 2.1
[Dua07] DUARTE, J. C.; MILIDIÚ, R. L.. Generalized boosting learning.
Technical Report 15/07, Departamento de Informática, PUC-Rio, 2007. 1.3
[Fay96] FAYYAD, U. M.; PIATETSKY-SHAPIRO, G. ; SMYTH, P.. From data
mining to knowledge discovery in databases. AI Magazine, 17:37–54,
1996. 1.1
[Fra82] FRANCIS, W. N.; KUCERA, H.. Frequency analysis of english
usage. Lexicon and grammar, 1982. 7.3
[Fre90] FREUND, Y.. Boosting a weak learning algorithm by majority.
In: COLT: PROCEEDINGS OF THE WORKSHOP ON COMPUTATIONAL
LEARNING THEORY, MORGAN KAUFMANN PUBLISHERS, 1990. 1.1,
2.1
[Fre95a] FREUND, Y.; SCHAPIRE, R. E.. A decision-theoretic generalization of on-line learning and an application to boosting. In: EUROPEAN CONFERENCE ON COMPUTATIONAL LEARNING THEORY, p.
23–37, 1995. 1.1, 2.2, 2.2.2, 2.3
[Fre96] FREUND, Y.; SCHAPIRE, R. E.. Experiments with a new boosting
algorithm. In: INTERNATIONAL CONFERENCE ON MACHINE LEARNING, p. 148–156, 1996. 2.3, 5.1
Referências Bibliográficas
83
[Fre05] FREITAS, M. C.; GARRAO, M.; OLIVEIRA, C.; DOS SANTOS, C. N.
; SILVEIRA, M.. A anotação de um corpus para o aprendizado
supervisionado de um modelo de sn. In: PROCEEDINGS OF THE III
TIL / XXV CONGRESSO DA SBC, São Leopoldo - RS - Brasil, 2005. 7.4
[Fre06] FREITAS, M. C.; DUARTE, J. C.; DOS SANTOS, C. N.; MILIDIÚ, R. L.;
RENTERIA, R. P. ; QUENTAL, V.. A machine learning approach
to the identification of appositives. In: PROCEEDINGS OF IBEROAMERICAN AI CONFERENCE, Ribeirão Preto, Brazil, October 2006. 1.3
[Fri95] FRITZKE, B.. A growing neural gas network learns topologies.
In: Tesauro, G.; Touretzky, D. S. ; Leen, T. K., editors, ADVANCES IN
NEURAL INFORMATION PROCESSING SYSTEMS 7, p. 625–632. MIT
Press, Cambridge MA, 1995. 4.3
PUC-Rio - Certificação Digital Nº 0510956/CA
[Hol75] HOLLAND, J. H.. Adaptation in Natural and Artificial Systems.
University of Michigan Press, Ann Arbor, 1975. 3.1
[Ida98] RATSCH, G.. Ida benchmark repository used in several boosting, kfd and svm papers, 1998. 6.3.2
[Kim04] KIM, H.; KIM, J.. Combining active learning and boosting
for naı̈ve bayes text classifiers. Advances in Web-Age Information
Management, p. 519–527, 2004.
[Kud01] KUDO, T.; MATSUMOTO, Y.. Chunking with support vector
machines. In: PROCEEDINGS OF THE NAACL-2001, 2001. 7.6.2
[Lut96] LUTZ, M.. Programming Python. oreilly, October 1996. 6.4
[Mac67] MACQUEEN, J. B.. Some methods for classification and analysis of multivariate observations. In: PROC. OF THE FIFTH BERKELEY SYMPOSIUM ON MATHEMATICAL STATISTICS AND PROBABILITY, volumen 1, p. 281–297. University of California Press, 1967. 4.3
[Man99] MANNING, C. D.; SCHÜTZE, H.. Foundations of Statistical
Natural Language Processing. MIT Press, Cambridge, MA, 1999. 6.2
[Mei03] MEIR, R.; RÄTSCH, G.. An introduction to boosting and leveraging. Advanced lectures on machine learning, p. 118–183, 2003.
[Mik99] MIKA, S.; RATSCH, G.; WESTON, J.; SCHOLKOPF, B. ; MULLERS,
K. R.. Fisher discriminant analysis with kernels. In: NEURAL
NETWORKS FOR SIGNAL PROCESSING IX, 1999. PROCEEDINGS OF
Referências Bibliográficas
84
THE 1999 IEEE SIGNAL PROCESSING SOCIETY WORKSHOP, p. 41–48,
1999. 6.5.2, 6.12, 6.5.2
[Mil06b] MILIDIÚ, R. L.; DUARTE, J. C. ; CAVALCANTE, R.. Machine
learning algorithms for portuguese named entity recognition. In:
Rezende, S. O.; da Silva Filho, A. C. R., editors, FOURTH WORKSHOP
IN INFORMATION AND HUMAN LANGUAGE TECHNOLOGY (TIL’06),
RIBEIRãO PRETO, BRAZIL, OCTOBER 23-28, 2006. ICMC-USP, 2006. 1.3
[Mil06] MILIDIÚ, R. L.; DOS SANTOS, C. N.; DUARTE, J. C. ; RENTERIA,
R. P.. Semi-supervised learning for portuguese noun phrase extraction. In: PROCEEDINGS OF 7TH WORKSHOP ON COMPUTATIONAL PROCESSING OF WRITTEN AND SPOKEN PORTUGUESE, p. 200–
203, Itatiaia, Brazil, May 2006. 1.3
PUC-Rio - Certificação Digital Nº 0510956/CA
[Mil07a] MILIDIÚ, R. L.; DUARTE, J. C. ; DOS SANTOS, C. N.. Tbl template selection: An evolutionary approach. In: PROCEEDINGS OF
CONFERENCE OF THE SPANISH ASSOCIATION FOR ARTIFICIAL INTELLIGENCE - CAEPIA, Salamanca, Spain, 2007. 1.3, 5.3
[Mil07b] MILIDIÚ, R. L.; DUARTE, J. C. ; DOS SANTOS, C. N.. Evolutionary
tbl template generation. Journal of the Brazilian Computer Society,
13(4):39–50, 2007. 1.3, 5.3
[Mil07c] MILIDIÚ, R. L.; DUARTE, J. C. ; CAVALCANTE, R.. Machine
learning algorithms for portuguese named entity recognition.
Inteligencia Artificial, Revista Iberoamericana de IA, 11(36):67–75, 2007. 1.3
[Mil08b] MILIDIÚ, R. L.; DOS SANTOS, C. N. ; DUARTE, J. C.. Portuguese
corpus-based learning using etl. Journal of the Brazilian Computer
Society, 14(4):17–27, 2008. 1.3
[Mil08a] MILIDIÚ, R. L.; DOS SANTOS, C. N. ; DUARTE, J. C.. Phrase chunking using entropy guided transformation learning. In: PROCEEDINGS OF THE 46TH ANNUAL MEETING OF THE ASSOCIATION FOR
COMPUTATIONAL LINGUISTICS: HUMAN LANGUAGE TECHNOLOGIES
- ACL-08: HLT, Columbus, Ohio, 2008. 1.3, 4.2.2, 5.4
[Mil09a] MILIDIÚ, R. L.; DUARTE, J. C.. Boosting at start. In: INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND APPLICATIONS, Innsbruck, Austria, 2009. IASTED. 1.3, 2
Referências Bibliográficas
85
[Mil09b] MILIDIÚ, R. L.; DUARTE, J. C.. Improving bas committee performance with a semi-supervised approach. In: EUROPEAN SYMPOSIUM ON ARTIFICIAL NEURAL NETWORKS - ADVANCES IN COMPUTATIONAL INTELLIGENCE AND LEARNING, ESANN 2009, Brugges,
Belgium, 4 2009. ESANN. 1.3, 2
[Mil09c] MILIDIÚ, R. L.; DUARTE, J. C.. Improving bas committee with
etl voting. In: ADVANCES IN MACHINE LEARNING AND CYBERNETICS, 8TH INTERNATIONAL CONFERENCE, ICMLC 2009, Baoding,
China, 7 2009. Springer. 1.3, 2
[Mit97] MITCHELL, T. M.. Machine Learning. McGraw-Hill, New York, 1997.
1.1
PUC-Rio - Certificação Digital Nº 0510956/CA
[Nis05] NIS, T.. Dictionary of algorithms and data structures, August
2005. 4.1.1
[PVS07] PVS, A.; GALI, K.. Part-of-speech tagging and chunking using
conditional random fields and transformation based learning. In:
PROCEEDINGS OF THE IJCAI AND THE WORKSHOP ON SHALLOW
PARSING FOR SOUTH ASIAN LANGUAGES (SPSAL), p. 21–24, 2007.
7.6.2
[Pol06] POLIKAR, R.. Ensemble based systems in decision making. IEEE
Circuits and Systems Magazine, 6(3):21–45, 2006. 1.1
[Qui86] QUINLAN, J. R.. Induction of decision trees. Machine Learning,
1(1):81–106, 1986. 7.5
[Ram95] RAMSHAW,
L.;
MARCUS,
M..
Text
chunking
using
transformation-based learning. In: NATURAL LANGUAGE PROCESSING USING VERY LARGE CORPORA. Kluwer, 1999. 7.4
[Rat99] RÄTSCH, G.; ONODA, T. ; MÜLLER, K. R.. Regularizing adaboost.
In: PROCEEDINGS OF THE 1998 CONFERENCE ON ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS II, p. 564–570, Cambridge,
MA, USA, 1999. MIT Press.
[Rat02] RÄTSCH, G.; WARMUTH, M. K.. Maximizing the margin with
boosting, 2002.
[Rat05] RÄTSCH, G.; WARMUTH, M. K.. Efficient margin maximizing
with boosting. J. Mach. Learn. Res., 6:2131–2152, 2005.
Referências Bibliográficas
86
[San05] DOS SANTOS, C. N.; OLIVEIRA, C.. Constrained atomic term:
Widening the reach of rule templates in transformation based
learning. In: EPIA, volumen 3808 de Lecture Notes in Computer
Science, p. 622–633. Springer, 2005.
[San09] DOS SANTOS, C. N.. Entropy guided transformation learning.
Master’s thesis, PUC-Rio, Rio de Janeiro - RJ, 3 2009. 5.4.1, 7.2, 7.6.1,
7.6.1, 7.6.1, 7.6.1, 7.6.2
[Sch90] SCHAPIRE, R. E.. The strength of weak learnability. Machine
Learning, 5:197–227, 1990. 1.1, 2.1
[Sch00] SCHAPIRE, R. E.; SINGER, Y.. Boostexter: A boosting-based
system for text categorization. Machine Learning, 39(2/3):135–168,
2000. 6
[Sch01] SCHAPIRE, R.. The boosting approach to machine learning: An
PUC-Rio - Certificação Digital Nº 0510956/CA
overview, 2001.
[Tjo00] SANG, E. F. T. K.; BUCHHOLZ, S.. Introduction to the conll-2000
shared task: chunking. In: PROCEEDINGS OF THE 2ND WORKSHOP
ON LEARNING LANGUAGE IN LOGIC AND THE 4TH CONLL, p. 127–132,
Morristown, NJ, USA, 2000. Association for Computational Linguistics. 7.4
[Tyc08] IEL-UNICAMP; IME-USP. Corpus anotado do português histórico
tycho brahe. http://www.ime.usp.br/˜tycho/corpus/. 7.3
[Uci98] NEWMAN, D. J.; HETTICH, S.; BLAKE, C. L. ; MERZ, C. J.. Uci
repository of machine learning databases, 1998. 1.3, 6.3.1
[War06] WARMUTH, M. K.; LIAO, J. ; RÄTSCH, G.. Totally corrective boosting algorithms that maximize the margin. In: ICML ’06: PROCEEDINGS OF THE 23RD INTERNATIONAL CONFERENCE ON MACHINE
LEARNING, p. 1001–1008, New York, NY, USA, 2006. ACM.
[War07] WARMUTH, M.; GLOCER, K. ; RÄTSCH, G.. Boosting algorithms
for maximizing the soft margin. In: Platt, J.; Koller, D.; Singer, Y. ;
Roweis, S., editors, ADVANCES IN NEURAL INFORMATION PROCESSING
SYSTEMS 20, p. 1585–1592. MIT Press, Cambridge, MA, 2008. 2.3
[Wil05] WILSON, G.; HEYWOOD, M.. Use of a genetic algorithm in brill’s
transformation-based part-of-speech tagger. In: PROCEEDINGS OF
THE 2005 CONFERENCE ON GENETIC AND EVOLUTIONARY COMPUTATION, p. 2067–2073, Washington DC, USA, 25-29 June 2005. ACM Press.
5.3
Referências Bibliográficas
87
[Wit99] WITTEN, I. H.; MOFFAT, A. ; BELL, T. C.. Managing Gigabytes:
Compressing and Indexing Documents and Images. Morgan
Kaufmann Publishers, San Francisco, CA, 1999. 4.1.2
[Wit02] WITTEN, I. H.; FRANK, E.. Data mining: practical machine learning tools and techniques with java implementations. SIGMOD
Rec., 31(1):76–77, 2002. 5.1
[Wu06] WU, Y.-C.; CHANG, C.-H. ; LEE, Y.-S.. A general and multi-lingual
PUC-Rio - Certificação Digital Nº 0510956/CA
phrase chunking model based on masking method. In: PROCEEDINGS OF 7TH INTERNATIONAL CONFERENCE ON INTELLIGENT
TEXT PROCESSING AND COMPUTATIONAL LINGUISTICS, p. 144–155,
2006. 7.6.2