Luís Torgo

My publications on DBLP and my profile on Google Scholar and on Semantic Scholar.


Books

  1. Torgo,L. (2017): Data Mining with R: Learning with Case Studies (2nd edition). Chapman and Hall/CRC . 426 pages. ISBN: 9781482234893
    (web site of the book)
  2. Torgo,L. (2012): Data Mining with R: Learning with Case Studies (Chinese Edition). China Machine Press. ISBN: 978-7-111-40700-3
  3. Torgo,L. (2010): Data Mining with R: Learning with Case Studies. Chapman and Hall/CRC. 305 pages. ISBN: 9781439810187
    (web site of the book)
  4. Torgo,L. (2009): A Linguagem R: programação para a análise de dados (in Portuguese). Escolar Editora. ISBN: 978-972-592-246-0

Book Editions

  1. João Gama, Rui Camacho, Pavel Brazdil, Alípio Jorge, Luís Torgo (Eds.) (2005): Machine Learning: ECML 2005, 16th European Conference on Machine Learning, Porto, Portugal, October 3-7, 2005, Proceedings. Lecture Notes in Computer Science 3720, Springer 2005, ISBN 3-540-29243-8
  2. Alípio Jorge, Luís Torgo, Pavel Brazdil, Rui Camacho, João Gama (Eds.) (2005): Knowledge Discovery in Databases: PKDD 2005, 9th European Conference on Principles and Practice of Knowledge Discovery in Databases, Porto, Portugal, October 3-7, 2005, Proceedings. Lecture Notes in Computer Science 3721, Springer 2005, ISBN 3-540-29244-6

Book Chapters

  1. Torgo,L. (2016) : Model Trees in Encyclopedia of Machine Learning and Data Mining, C.Sammut and G.I.Webb (Eds.). Springer, 2016. ISBN: 978-1-4899-7502-7; DOI 10.1007/978-1-4899-7502-7_558-1
  2. Torgo,L. (2016) : Regression Trees in Encyclopedia of Machine Learning and Data Mining, C.Sammut and G.I.Webb (Eds.). Springer, 2016. ISBN: 978-1-4899-7502-7; DOI 10.1007/978-1-4899-7502-7_717-1
  3. Torgo,L. (2011) : Model Trees in Encyclopedia of Machine Learning, C.Sammut and G.I.Webb (Eds.). Pages 684--686, Springer, 2011. ISBN: 978-0-387-30768-8
  4. Torgo,L. (2011): Regression Trees in Encyclopedia of Machine Learning, C.Sammut and G.I.Webb (Eds.). Pages 842--845, Springer, 2011. ISBN: 978-0-387-30768-8
  5. Torgo,L. and Soares,C. (2010): Resource-bounded Outlier Detection Using Clustering Methods in Data Mining for Business Applications, C.Soares and R. Ghani (Eds.). Pages 84--98, IOS Press (2010). ISBN: 978-1607506324)
  6. P. Flach, H. Blockeel, T. Gartner, M Grobelnik, B. Kavsek, M. Kejkula, D. Krzywania, N. Lavrac, P. Ljubic, D. Mladenic, S. Moyle, S. Raeymaekers, J. Rauch, S. Rawles, R. Ribeiro, G. Sclep, J. Struyf, L. Todorovski, L. Torgo, D. Wettschereck, and S. Wu (2003). On the road to knowledge: mining 21 years of UK traffic accident reports, chapter 12 of Data Mining and Decision Support, Integration and Collaboration, D. Mladenic et. al. (eds.). Morgan Kaufmann, ISBN 1-4020-7388-7
  7. Hellstrom, T. and Torgo,L. (2002): Post Processing Trading Signals for Improved Trading Performance, in Data Mining III, A. Zanasi et. al (eds.), pp 437-447, Wit Press.
  8. Brazdil, P.; Torgo, L. (1990) : Knowledge Acquisition via Knowledge Integration, in Current Trends in Knowledge Acquisition, Wielinga, B. et al (eds.), IOS Press.
    (Abstract)

Journals

  1. M. Monteiro and J. Séneca and L. Torgo et al. (2017). Environmental controls on estuarine nitrifying communities along a salinity gradient. Aquatic Microbial Ecology, 80-2, pp. 167-180. publisher document
  2. N. Moniz and L. Torgo and M. Eirinaki and P. Branco (2017). A Framework for Recommendation of Hughly Popular News Lacking Social Feedback. New Generation Computing, 35-4, pp. 417-450. publisher document
  3. N. Moniz and P. Branco and L. Torgo (2017). Resampling strategies for imbalanced time series forecasting.. International Journal of Data Science and Analytics, 3-3, pp. 417-450. publisher document
  4. Nuno Moniz and Luis Torgo and João Vinagre (2016). Data-driven Relevance Judgments for Ranking Evaluation. CoRR abs/1612.06136.
  5. Luis Baía and Luis Torgo (2016). A comparative study of approaches to forecast the correct trading actions. Expert Systems. DOI: 10.1111/exsy.12169
  6. Paula Branco, Luis Torgo and Rita Ribeiro (2016). A Survey of Predictive Modeling on Imbalanced Domains. ACM Comput. Surv. 49, 2, Article 31 (August 2016), 50 pages. DOI: http://dx.doi.org/10.1145/2907070
  7. Paula Branco, Rita Ribeiro and Luis Torgo (2016). A UBL: an R package for Utility-based Learning. CoRR abs/1604.08079.
  8. Nuno Moniz and Luis Torgo (2015). Socially Driven News Recommendation. CoRR abs/1506.01743
  9. Paula Branco, Luis Torgo and Rita Ribeiro (2015). A Survey of Predictive Modelling under Imbalanced Distributions. CoRR abs/1505.01658.
  10. Luis Torgo (2014). An Infra-Structure for Performance Estimation and Experimental Comparison of Predictive Models in R. CoRR abs/1412.0436.
    (Github Site of the package)(CRAN site of the package )
  11. Luis Torgo, Paula Branco, Rita P. Ribeiro and Bernhard Pfahringer (2015). Re-sampling Strategies for Regression. Expert Systems, vol. 32 (3), pp. 465-476. DOI: 10.1111/exsy.12081
    (Site with code and data to reproduce the work)
  12. Joaquin Vanschoren, Jan N. van Rijn, Bernd Bischl, and Luis Torgo (2013). OpenML: networked science in machine learning. SIGKDD Explorations Newsletter, vol. 15, issue 2, (Dec 2013), 49-60. (DOI=10.1145/2641190.2641198)
    PDF on Github
  13. Drury,B., Torgo,L. and Almeida, J.J. (2012): Classifying News Stories with a Constrained Learning Strategy to Estimate the Direction of a Market Index International Journal of Computer Science & Applications, vol. 9 - 1, pp. 1-22. Technomathematics Research Foundation, ISSN 0972-9038
  14. Herrera,M.; Torgo,L.; Izquierdo,J. and Garcia, R. (2010): Predictive models for forecasting hourly urban water demand. Journal of Hydrology Volume 387, Issues 1-2, pp. 141-150. Elsevier
  15. Torgo,L. (2009): Deteção de Fraude usando o R: um caso de estudo. Boletim da Sociedade Portuguesa de Estatística, Outubro de 2009
  16. Ribeiro,R., Torgo,L. (2008): A Comparative Study on Predicting Algae Blooms in Douro River, Portugal.Ecological Modelling, vol.212 (1-2), pp. 86-91. Elsevier
  17. Silva,A., Jorge,A. and Torgo,L. (2006): Design of an end-to-end method to extract information from tables. International Journal on Document Analysis and Recognition, vol. 8 (2-3), p. 144-171. Springer.
  18. Torgo,L., and Pinto da Costa,J. (2003): Clustered Partial Linear Regression. Machine Learning, 50 (3), pp. 303-319. Kluwer Academic Publishers.
    (Abstract)
  19. L. Torgo (2000). Thesis: Inductive learning to tree-based regression models. AI Communications, 13(2):137-138, IOS Press.
  20. Torgo,L. and Gama,J. (1997): Regression using Classification Algorithms.Intelligent Data Analysis , Vol. 1, No. 4.

International Conferences with Peer Reviewing

  1. V. Cerqueira and L. Torgo and F. Pinto and C.Soares (2017): Arbitrated Ensemble for Time Series Forecasting, in Proceedings of ECML/PKDD'2017. Springer.
    Best Student Machine Learning Paper Award
  2. P. Branco and Luis Torgo and R.P. Ribeiro (2017): Exploring Resampling with Neighborhood Bias on Imbalanced Regression Problems, in 18th Portuguese Conference on Artificial Intelligence, EPIA 2017. (c) Springer
  3. V. Cerqueira and L. Torgo and C.Soares (2017): Arbitrated Ensemble for Solar Radiation Forecasting, in Proceedings of IWANN'2017. LNCS, vol. 10305, pp. 720-732. Springer.
  4. P. Branco and Luis Torgo and R.P. Ribeiro (2017): Relevance-based Evaluation Metrics for Multi-Class Imbalanced Domains, in 21th Pacific-Asia Conference, PAKDD 2017. LNCS, vol. 10234, pp. 698-710. Springer.
  5. N. Guimarães, L. Torgo and A. Figueira (2016): Lexicon Expansion System for Domain and Time Oriented Sentiment Analysis
    Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, ISBN 978-989-758-203-5, pages 463-471
  6.  
  7. Mariana Oliveira, Luis Torgo and Vítor Santos Costa (2016): Predicting Wildfires: Propositional and Relational Spatio-Temporal Pre-Processing Approaches
    Proceedings of Discovery Science 2016
  8. Nuno Moniz, Paula Branco and Luis Torgo (2016): Resampling Strategies for Imbalanced Time Series
    Proceedings of DSAA 2016
  9. A. Martins et. al. (2016): MarinEye - a tool for marine monitoring
    Proceedings of IEEE OCEANS 2016. DOI: 10.1109/OCEANSAP.2016.7485624.
  10. C. Magalhães et. al. (2016): Development of an autonomous system for integrated marine monitoring
    Proceedings of 41st CIESM Congress
  11. C. Magalhães et. al. (2016): Distribution and Environmental Controls on Marine Nitrogen Biogeochemical Functions
    Proceedings of 41st CIESM Congress
  12. C. Magalhães, C. Lee, M. Monteiro, L. Torgo and C. Cary (2016): Everything is not everywhere: Antarctica Dry Valleys as an extreme counter example
    Proceedings of XXIV SCAR
  13. J. Seneca, C. Magalhães, M. Monteiro, C. Lee, L. Torgo and C. Cary (2016): Distribution of prokaryotic communities and NifH gene diversity in the extrem Darwin Mountains, Abtarctica
    Proceedings of XXIV SCAR
  14. M. Monteiro, J. Seneca, L. Torgo, M. Baptista, C. Lee, C. Cary and C. Magalhães (2016): The impact of environmental changes on nitrifyng communities from the Dry Valleys of Antarctica
    Proceedings of XXIV SCAR
  15. H. Ribeiro et. al. (2016): Development and validation of an autonomous filtration system for integrated marine monitoring
    Proceedings of ECSA56 - Coastal systems in transition: From a 'natural' to an 'anthropogenically-modified' state
  16. C. Bartilotti et. al. (2016): Presenting the MarinEye project –Development and validation of a prototype for multitrophic oceanic monitoring
    Proceedings of ASC 2016- ICES Anual Science Meeting
  17. A. dos Santos et. al. (2016): MarinEye – A prototype for multitrophic oceanic monitoring
    Proceedings das 4as Jornadas de Engenharia Hidrogrí¡fica
  18. C. Magalhães et. al. (2016): Development of an autonomous system for integrated marine monitoring
    Proceedings of XIX Iberian Symposium on Marine Biology Studies
  19. Leona Nezvaloví¡, Lubos Popelínsky, Luis Torgo, Karel Vaculík (2015): Class-Based Outlier Detection: Staying Zombies or Awaiting for Resurrection?, Proceeedings of IDA'2015, p.193-204
  20. Luis Baia and Luis Torgo (2015): Forecasting the Correct Trading Actions, in 17th Portuguese Conference on Artificial Intelligence, EPIA 2015, p. 560-571. (c) Springer
  21. Mariana Oliveira and Luis Torgo (2014): Ensembles for Time Series Forecasting, in Proceedings of Asian Conference on Machine Learning (ACML'2014). JMLR: Workshop and Conference Proceedings, vol. 39, 360-370.
    (Site with code and data to reproduce the work)
  22. Mariana Oliveira and Luis Torgo (2014): Ensembles for Time Series Forecasting (abstract),
    proceedings of late breaking papers of Discovery Science 2014
  23. Nuno Moniz, Luis Torgo and Fí¡tima Rodrigues (2014): Resampling approaches to improve news importance prediction,
    in Advances in Intelligent Data Analysis XIII (IDA'2014), Blockeel et. al. (eds.), pp. 215-226, LNCS vol. 8819, Springer
    (Link to published manuscript)
  24. Nuno Moniz and Luis Torgo (2014): Improvement of News Ranking through Importance Prediction,
    proceeding of KDD'2014 workshop NewsKDD - Data Science for News Publishing. DOI: 10.13140/2.1.4035.3282
  25. Luis Torgo and Rita P. Ribeiro and Bernhard Pfahringer and Paula Branco (2013): SMOTE for Regression,
    in 16th Portuguese Conference on Artificial Intelligence, EPIA 2013, pp. 378-389. (c) Springer
    (Link to published manuscript) (Site with associated material to reproduce the work)
  26. Jan N. van Rijn and Venkatesh Umaashankar and Simon Fischer and Bernd Bischl and Luis Torgo and Bo Gao and Patrick Winter and Bernd Wiswedel and Michael R. Berthold and Joaquin Vanschoren (2013): A RapidMiner extension for Open Machine Learning, in Proceedings of RCOMM'2013, 59-70. ISBN: 978-3-8440-2145-5
  27. Jan N. van Rijn and Bernd Bischl and Luis Torgo and Bo Gao and Venkatesh Umaashankar and Simon Fischer and Patrick Winter and Bernd Wiswedel and Michael R. Berthold and Joaquin Vanschoren (2013): OpenML: A Collaborative Science Platform, in Proceedings of ECML/PKDD'2013, pp. 645-649. Springer.
  28. Ohashi,O. and Torgo,L. (2012): Spatial Interpolation using Multiple Regression, in ICDM 2012 - IEEE International Conference on Data Mining, pp. 1044--1049. (c) IEEE Computer Society
    (Link to published manuscript) (Site with associated material to reproduce the work)
  29. Ohashi,O. and Torgo,L. (2012): Wind speed forecasting using spatio-temporal indicators, in Proceedings of ECAI 2012 - 20th European Conference on Artificial Intelligence.
  30. Drury,B., Torgo,L. and Almeida, J.J. (2011): Guided Self Training for Sentiment Classification, in Proceedings of International Conference On Recent Advances in Natural Language Processing (RANLP 2011) - ROBUS workshop. Hissar, Bulgaria, September 12-14.
  31. Drury,B. and Dias,G. and Torgo,L. (2011): Contextual Classification Strategy for Polarity Classification of Direct Quotations from Financial News, in International Conference On Recent Advances in Natural Language Processing (RANLP 2011). Hissar, Bulgaria, September 12-14.
  32. Torgo,L. and Ohashi,O. (2011) : 2D-Interval Predictions for Time Series, in Proceedings of 17th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'2011)
    (Site with associated material to reproduce the work)
  33. Torgo,L. and Lopes,E. (2011): Utility-based Fraud Detection, in Proceedings of 22th International Joint Conference on Artificial Intelligence (IJCAI'2011), p. 1517-1522. AAAI Press.
    (Site with associated material to reproduce the work)
  34. Drury,B. and Torgo,L. and Almeida, J.J. (2011) : Classifying News Stories to Estimate the Direction of a Stock Market Index, in Proceedings of CISTI'2011.
  35. Ohashi,O., Torgo,L. and Ribeiro,R. (2010): Interval Forecast of Water Quality Parameters, in ECAI 2010 - 19th European Conference on Artificial Intelligence, edited by H. Coelho, R. Studer and M. Wooldridge. Vol. 215, pp. 283-288. IOS Press. ISBN 978-1-60750-605-8
  36. Torgo,L. and Ribeiro,R. (2009): Precision and Recall for Regression, in Proceedings of the 12th International Conference on Discovery Science (DS'2009). LNAI - 5808. Springer.
  37. Torgo,L. and Pereira,W. and Soares,C. (2009): Detecting Errors in Foreign Trade Transactions: Dealing with Insufficient Data, in 14th Portuguese Conference on Artificial Intelligence, EPIA 2009, Lopes, L. et. al (eds.). LNAI - 5816, (c) Springer.
  38. Ribeiro,R., Torgo,L. (2008): Utility-based performance measures for regression, Proceedings of the 3rd Workshop on Evaluation Methods for Machine Learning, in conjunction with the 25th International Conference on Machine Learning (ICML 2008).
  39. Torgo,L. (2007): Resource-bounded Fraud Detection, in Progress in Artificial Intelligence, 13th Portuguese Conference on Artificial Intelligence, EPIA 2007, Neves et. al (eds.). LNAI 4874, pages 449-460. Springer
  40. Torgo,L., Ribeiro,R. (2007) : Utility-based Regression, in Knowledge Discovery in Databases: PKDD 2007, 11th European Conference on Principles and Practice of Knowledge Discovery in Databases. Kok et. al (eds.) LNAI 4702, Springer.
  41. Barbosa,J., Torgo,L. (2006) : Online ensembles for financial trading, in Proceedings of the Workshop on Pratical Data Mining: applications, experiences and challenges, ECML/PKDD'2006.
  42. Ribeiro,R., Torgo,L. (2006): Rule-based Prediction of Rare Extreme Values, in Proceedings of the 9th International Conference on Discovery Science (DS'2006). Lecture Notes in Artificial Intelligence - 4265. Springer.[BEST STUDENT PAPER AWARD]
  43. Torgo,L., Ribeiro,R. (2006): Predicting Rare Extreme Values, in Proceedings of the 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'2006). W. Ng et al. (eds.). Lecture Notes in Artificial Intelligence - 3918. Springer.
    (extended Internal Report)
  44. Torgo,L., Marques,J. (2005): Adapting Peepholing to Regression Trees, in Proceedings of the 12th EPIA. Springer.
  45. Torgo,L. (2005): Regression Error Characteristic Surfaces, in Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2005). Chicago, USA. ACM.
  46. Torgo,L. (2005): The TNT Financial Trading System: a midterm report, in Proceedings of the Workshop on Data Mining for Business at ECML/PKDD 2005. Porto, Portugal.
  47. Ribeiro,R., Torgo,L. (2005): A Comparative Study on Predicting Algae Blooms in River Douro, Portugal, in Proceedings of the V European Conference on Ecological Modelling (ECEM-2005). Pushshino, Russia.
  48. Loureiro,A., Torgo,L., and Soares,C. (2004): Outlier Detection using Clustering Methods: a data cleaning application, in Proceedings of KDNet Symposium on Knowledge-based Systems for the Public Sector. Bonn, Germany.
  49. Silva,A., Jorge,A. and Torgo,L. (2003): Selection of Table Areas for Information Extraction, in Proceedings of the 3rd International Workshop in Document Analysis and its Applications (DLIA 2003).
  50. Silva,A., Jorge,A. and Torgo,L. (2003): 564378 bytesAutomatic Selection of Table Areas in Documents for Information Extraction, in Proceedings of Portuguese AI Conference (EPIA'03). Lecture Notes in Artificial Intelligence - 2902. (c) Springer-Verlag.
  51. Ribeiro,R. and Torgo,L. (2003): Predicting Harmful Algae Blooms, in Proceedings of Portuguese AI Conference (EPIA'03). Lecture Notes in Artificial Intelligence - 2902. (c) Springer-Verlag.
  52. Torgo,L., and Ribeiro,R. (2003) : Predicting Outliers, in Proceedings of Principles of Data Mining and Knowledge Discovery (PKDD'03). Lavrac,N. et al. (eds.). LNAI 2838, (c) Springer-Verlag.
  53. Torgo,L. (2002): Computationally Efficient Linear Regression Trees, in Classification, Clustering and Data Analysis: recent advances and applications (Proceed. of IFCS 2002), Jajuga,K. et.al. (eds.). Studies in classification, data analysis, and knowledge organization. (c) Springer.
    (Abstract)
  54. Torgo,L. (2001): A study on end-cut preference in least squares regression trees, in Proceedings of the Portuguese AI Conference (EPIA 2001), Brazdil,P. and Jorge,A. (eds.), LNAI 2258, (c) Springer-Verlag.
    (Abstract)
  55. Almeida,P. and Torgo,L. (2001): The Use of Domain Knowledge in Feature Construction for Financial Time Series Prediction, in Proceedings of the Portuguese AI Conference (EPIA 2001), Brazdil,P. and Jorge,A. (eds.), LNAI 2258, Springer-Verlag.
  56. Torgo,L. (2000): Partial Linear Trees, in Proceedings of the 17th International Conference on Machine Learning (ICML 2000). Langley,P. (ed.). Pages 1007-1014. Morgan Kaufmann Publishers.
    (Abstract)
  57. Torgo,L., and Pinto da Costa,J. (2000): Clustered Partial Linear Regression, in Proceedings of the Fifth International Workshop on Multistrategy Learning (MSL-2000), Guimarães, Portugal, June, 2000.
    (Abstract)
  58. Torgo,L., and Pinto da Costa,J. (2000) : Clustered Partial Linear Regression, in Proceedings of the 11th European Conference on Machine Learning (ECML 2000). Lopez de Mantaras,R. and Plaza,E. (eds.). LNAI 1810, p.426-436. (c) Springer-Verlag.
    (Abstract)
  59. Torgo,L., and Pinto da Costa,J. (2000): Clustered Multivariate Regression, in Data Analysis, Classification, and Related Methods. Kiers et al. (eds.). Studies in Classification, Data Analysis, and Knowledge Organization. (c) Springer-Verlag.
    (Abstract)
  60. Torgo,L. (2000): Efficient and Comprehensible Local Regression, in Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2000). Terano et al. (eds.). LNAI 1805, p. 376-379. (c) Springer-Verlag.
    (Abstract)
    (you may find a longer version of this work in this internal report, 66328 bytes in format ".ps.gz")
  61. Torgo,L. (1999): Predicting the Density of Algae Communities using Local Regression Trees, in Proceedings of the European Congress on Intelligent Techniques and Soft Computing (EUFIT'99)
    (invited paper as a consequence of the participation on the 3rd International ERUDIT Competition)
    (Abstract)
  62. Torgo,L. (1998): A Comparative Study of Reliable Error Estimators for Pruning Regression Trees, in Proceedings of the Iberoamericam Conference on AI (IBERAMIA-98), Coelho,H. (ed.).
    (Abstract)(HTML version)
  63. Gama,J.; Torgo,L. and Soares,C. (1998): Dynamic Discretization of Continuous Attributes, in Proceedings of the Iberoamericam Conference on AI (IBERAMIA-98), Coelho,H. (ed.).
    (Abstract)
  64. Torgo,L. (1998): Error Estimates for Pruning Regression Trees, in Proc. of the 10th European Conference on Machine Learning (ECML-98), Nedellec,C. and Rouveirol,C. (eds.). LNAI 1398, Springer Verlag.
    (Abstract)
  65. Torgo,L. (1997): Functional Models for Regression Tree Leaves, in Proceedings of the International Machine Learning Conference (ICML-97), Fisher,D.(ed.), Morgan Kaufmann Publishers.
    (Abstract)(HTML version)
  66. Torgo,L. and Gama,J. (1997) : Search-based Class Discretization, in Proceedings of the European Conference on Machine Learning (ECML-97). Lecture Notes in Artificial Intelligence 1224, Springer Verlag.
    (Abstract)(HTML version)
  67. Torgo,L.(1997) : Kernel Regression Trees, Poster papers of the European Conference on Machine Learning (ECML-97), Internal Report of Faculty of Informatics and Statistics, University of Economics, Prague. ISBN:80-7079-368-6.
  68. Torgo,L. and Gama,J. (1996) : Regression by Classification, in Proceedings of the Brasilian AI Symposium (SBIA'96), BorgesD., Kaestner,C.(eds.), Lecture Notes in Artificial Intelligence 1159, Springer Verlag.
    (Abstract)(HTML version)
  69. Torgo,L. (1995) : Applying Propositional Learning to Time Series Prediction, in Workshop on Statistics, Machine Learning and Knowledge Discovery in Databases, edited by Kodratoff, Y. et. al, that took place at the European Conference on Machine Learning, ECML-95.
    (Abstract)(HTML version)
  70. Torgo,L. (1995) : Data Fitting with Rule-based Regression, Proceedings of the workshop on Artificial Intelligence Techniques (AIT'95), Zizka,J. & Brazdil,P. (eds.), Brno, Czech Republic.
    (Abstract)(HTML version)
  71. Torgo,L. (1993) : Controlled Redundancy in Incremental Rule Learning, in Proceedings of the European Conference on Machine Learning (ECML-93), Brazdil,P.(ed.), Lecture Notes in Artificial Intelligence 667, Springer Verlag.
    (Abstract) (HTML version)
  72. Torgo,L. (1993) : Rule Combination in Inductive Learning, in Proceedings of the European Conference on Machine Learning (ECML-93), Brazdil,P.(ed.), Lecture Notes in Artificial Intelligence 667, Springer Verlag.
    (Abstract) (HTML version)
  73. Torgo,L.; Kubat,M. (1991) : Knowledge Integration and Forgetting, in Proceedings of the Checoslovak AI Conference in 1991, Prague.
    (Abstract) (HTML version)
  74. Brazdil, P.; Gams, M.; Sian, S.; Torgo, L.; Van de Velde,W. (1991): Learning in Distributed Systems and Multi-Agent Environments, in Machine Learning: EWSL-91 (European Working Session on Learning), Y. Kodratoff (Ed.), Lecture Notes in Artificial Intelligence, Springer-Verlag
    (Abstract)(HTML version)

Theses

  1. Torgo, L. (1999): Inductive Learning of Tree-based Regression Models. PhD on Computer Science. Department of Computer Science of the Faculty of Sciences. University of Porto.
Luís Torgo