Publications

Metalearning

  1. Smith, M.R., Martinez, T. and Giraud-Carrier, C. (2015). The Potential Benefits of Data Set Filtering and Learning Algorithm Hyperparameter Optimization. In Proceedings of the ECML Workshop on Meta-Learning and Algorithm Selection, CEUR 1455, 3-14.
  2. Ridd, P. and Giraud-Carrier, C. (2014). Using Metalearning to Predict When Parameter Optimization Is Likely to Improve Classification Accuracy. In Proceedings of the ECAI Workshop on Meta-Learning and Algorithm Selection, CEUR 1201, 18-23.
  3. Smith, M., Mitchell, L., Giraud-Carrier, C. and Martinez, T. (2014). Recommending Learning Algorithms and Their Hyperparameters. In Proceedings of the ECAI Workshop on Meta-Learning and Algorithm Selection, CEUR 1201, 39-40.
  4. Smith, M., White, A., Giraud-Carrier, C. and Martinez, T. (2014). An Easy to Use Repository for Comparing and Improving Machine Learning Algorithm Usage. in Proceedings of the ECAI Workshop on Meta-Learning and Algorithm Selection, CEUR 1201, 41-48.
  5. Smith, M.R., Martinez, T. and Giraud-Carrier, C. (2014). An Instance Level Analysis of Data Complexity. Machine Learning, 95(2):225-256.
  6. Lee, J. and Giraud-Carrier, C. (2014). On the Dangers of Default Implementations: The Case of Radial Basis Function Networks. Intelligent Data Analysis, 18(2):261-279.
  7. Lee, J. and Giraud-Carrier, C. (2013). Automatic Selection of Classification Learning Algorithms for Data Mining Practitioners. Intelligent Data Analysis, 17(4):665-678.
  8. Brazdil, P., Vilalta, R., Soares, C. and Giraud-Carrier, C. (2012). Metalearning. In Seel, N.M. (Ed.), Encyclopedia of the Science of Learning, 762.
  9. Lee, J. and Giraud-Carrier, C. (2011). A Metric for Unsupervised Metalearning. Intelligent Data Analysis, 15(6):827-841.
  10. Brazdil, P., Vilalta, R., Giraud-Carrier, C. and Soares, C. (2011). Metalearning. In Sammut, C. and Webb, G.I. (Eds.), Encyclopedia of Machine Learning, 662-666.
  11. Brazdil, P., Giraud-Carrier, C., Soares, C., and Vilalta, R. (2009). Metalearning: Applications to Data Mining. Springer.
  12. Lee, J. and Giraud-Carrier, C. (2008). New Insights Into Learning Algorithms and Datasets. In Proceedings of the Seventh International Conference on Machine Learning and Applications, 135-140.
  13. Lee, J. and Giraud-Carrier, C. (2008). Predicting Algorithm Accuracy with a Small Set of Effective Meta-Features. In Proceedings of the Seventh International Conference on Machine Learning and Applications, 808-812.
  14. Giraud-Carrier, C., Brazdil, P., Soares, C. and Vilalta, R. (2008). Meta-learning. In Wang, J. (Ed.), Encyclopedia of Data Warehousing and Mining, 2nd Edition, 1207-1215.
  15. Giraud-Carrier, C. and Vilalta, R. (2007). Proceedings of the IJCNN-2007 Workshop on Meta-learning.
  16. Oviatt, D., Frandsen, D., Clements, K. and Giraud-Carrier, C. (2008). An Instance-based Nearest-neighbor Approach to Classifying Nuclear Explosion Data. In Summary Booklet of the Data Mining Contest at the IEEE International Conference on Data Mining, 12-15.
  17. Lee, J. and Giraud-Carrier, C. (2007). Transfer Learning in Decision Trees. In Proceedings of the International Joint Conference on Neural Networks, #1208.
  18. Giraud-Carrier, C., Vilalta, R. and Brazdil, P. (2005). Proceedings of the ICML-2005 Workshop on Meta-learning.
  19. Giraud-Carrier, C. and Ventura, D. (2005). Effecting Transfer via Learning Curve Analysis. In Proceedings of the NIPS'2005 Workshop on Inductive Transfer: 10 Years Later.
  20. Giraud-Carrier, C. (2005). The Data Mining Advisor: Meta-learning at the Service of Practitioners. In Proceedings of the 4th International Conference on Machine Learning Applications, 113-119. (Contact author for reprint).
  21. Giraud-Carrier, C. and Provost, F. (2005). Toward a Justification of Meta-learning: Is the No Free Lunch Theorem a Show-stopper? In Proceedings of the ICML-2005 Workshop on Meta-learning, 12-19.
  22. Vilalta, R., Giraud-Carrier, C. and Brazdil, P. (2005). Meta-learning. In Maimon, O. and Rokach, L. (Eds.), Data Mining and Knowledge Discovery Handbook, Springer, 731-748. (Contact author for reprint).
  23. Vilalta, R., Giraud-Carrier, C., Brazdil, P. and Soares, C. (2004). Using Meta-Learning to Support Data-Mining. International Journal of Computer Science Applications, Vol. I, No. 1, 31-45.
  24. Giraud-Carrier, C., Brazdil, P. and Vilalta, R. (2004). Special Issue on Meta-learning. Machine Learning, 54(3).
  25. Giraud-Carrier, C. and Keller, J. (2002). Meta-learning. In Dealing with the Data Flood: Mining Data, text and Multimedia, Meij, J. (Ed.), STT 65, STT/Beweton, The Hague, 832-844.
  26. Bensusan, H. and Giraud-Carrier, C. (2000). Discovering Task Neighbourhoods through Landmark Learning Performances. In Proceedings of the 4th European Conference on Principles and Practice of Knowledge Discovery in Databases, 325-331.
  27. Bensusan, H. and Giraud-Carrier, C. (2000). Harmonia Loosely Praestabilita: Discovering Adequate Inductive Strategies. In Proceedings of the 22nd Annual Meeting of the Cognitive Science Society, 609-614.
  28. Bensusan, H., Giraud-Carrier, C. and Kennedy, C. (2000). A Higher-order Approach to Meta-learning. In Proceedings of the ECML'2000 workshop on Meta-Learning: Building Automatic Advice Strategies for Model Selection and Method Combination, 109-117.
  29. Bensusan, H. and Giraud-Carrier, C. (2000). Casa Batlo is in Passeig de Gracia or Landmarking the Expertise Space. In Proceedings of the ECML'2000 workshop on Meta-Learning: Building Automatic Advice Strategies for Model Selection and Method Combination, 29-47.
  30. Pfahringer, B., Bensusan, H. and Giraud-Carrier, C. (2000). Meta-learning by Landmarking Various Learning Algorithms. In Proceedings of the 17th International Conference on Machine Learning, 743-750.
  31. Bensusan, H., Giraud-Carrier, C.and Pfahringer, B. (2000). What Works Well Tells Us What Works Better. In Proceedings of ICML'2000 Workshop on What Works Well Where, 1-8.
  32. Giraud-Carrier, C. (1998). Beyond Predictive Accuracy: What?. In ECML'98 Workshop Notes - Upgrading Learning to the Meta-Level: Model Selection and Data Transformation, 78-85.

Computational Health Science

  1. Braithwaite, S.R., Giraud-Carrier, C., West, J., Barnes, M.D. and Hanson, C.L. (2016). Validating Machine Learning Algorithms for Twitter Data Against Established Measures of Suicidality. JMIR Mental Health, 3(2):e21.
  2. Chary, M., Genes, N., Giraud-Carrier, C., Hanson, C., Nelson, L., and Manini, A.F. (2015). Estimating nonmedical use of prescription opioids in the United States from social media. European Association of Poison Centres and Clinical Toxicologists Annual Congress, Short oral presentation and Poster. (abstract published in Clinical Toxicology).
  3. Smiley, S., Nielsen, J., Gurgel, R., Zielinski, B., Wright, B., Wang, A., Auduong, P., Foster, N., Giraud-Carrier, C., and Anderson, J. (2014). Validation of quantitative regional atrophy dementia classification in a large clinical MRI sample. Organization for Human Brain Mapping (OHBM) Annual Meeting, Poster #1119. Available online at ww4.aievolution.com/hbm1401.
  4. Seeley, M., Clement, M., Giraud-Carrier, C., Snell, Q., Bodily, P., Fujimoto, S., Kauwe, J., Ridge. P.G. (2014) A Structured Approach to Ensemble Learning for Alzheimer’s Disease Prediction. In Proceedings of the Fifth ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, 605-606.
  5. Smiley, S., Nielsen, J., Gurgel, R., Zielinski, B., Wright, B., Wang, A., Auduong, P., Foster, N., Giraud-Carrier, C. and Anderson, J. (2014). Validation of Quantitative Regional Atrophy Dementia Classification in a Large Clinical MRI Sample. Poster at Organization for Human Brain Mapping (OHBM) Annual Meeting, #1119.
  6. Jashinsky, J., Burton, S.H., Hanson, C.L., West, J., Giraud-Carrier, C., Barnes, M.D. and Argyle, T. (2014). Tracking Suicide Risk Factors Through Twitter in the US. Crisis: The Journal of Crisis Intervention and Suicide Prevention, 35(1):50-59.
  7. Burton, S.H., Morris, R.G., Giraud-Carrier, C.G., West, J.H., Thackeray, R. (2014). Mining Useful Association Rules from Questionnaire Data. Intelligent Data Analysis, 18(3):479-494.
  8. Thackeray, R., Burton, S.H., Giraud-Carrier, Rollins, S. and Draper, C.R. (2013). Using Twitter for Breast Cancer Prevention: An Analysis of Breast Cancer Awareness Month. BMC Cancer, 13:508.
  9. Hanson, C.L., Cannon, B., Burton, S.H. and Giraud-Carrier, C. (2013). An Exploration of Social Circles and Prescription Drug Abuse through Twitter. Journal of Medical Internet Research, 15(9):e189.
  10. Burton, S.H., Tew, C.V., Cueva, S.S., Giraud-Carrier, C.G., Thackeray, R. (2013). Social Moms and Health: A Multi-platform Analysis of Mommy Communities. In Proceedings of the IEEE/ACM Conference on Advances in Social Networks Analysis and Mining (ASONAM), 169-174.
  11. Hanson, C.L., Burton, S., Giraud-Carrier, C., West, J., Barnes, M., Hansen, B. (2013). Tweaking and Tweeting: Exploring Twitter for Nonmedical Use of a Psychostimulant Drug (Adderall) Among College Students. Journal of Medical Internet Research, 15(4):e62.
  12. Neiger, B., Thackeray, R., Burton, S., Giraud-Carrier, C., Fagen, M. (2013). Evaluating Social Media's Capacity to Develop Engaged Audiences in Health Promotion Settings: Use of Twitter Metrics as a Case Study. Health Promotion Practice, 14(2):157-162.
  13. Lee, J. and Giraud-Carrier, C. (2013). Results on Mining NHANES Data: A Case Study in Evidence-based Medicine. Computers in Biology and Medicine, 43(5):493-503.
  14. Burton, S.H., Tanner, K.W., Giraud-Carrier, C.G., West, J.H., Barnes, M.D. (2012). "Right Time, Right Place" Health Communication on Twitter: Value and Accuracy of Location Information. Journal of Medical Internet Research, 14(6):e156.
  15. Neiger, B.L., Thackeray, R., Burton, S.H., Giraud-Carrier, C. and Eagen, M.C. (2013). Evaluating Social Media's Capacity to Develop Engaged Audiences in Health Promotion Settings: Use of Twitter Metrics as a Case Study. Health Promotion Practice, 14(2):157-162.
  16. West, J.H., Hall, P.C., Hanson, C.L., Giraud-Carrier, C., Neeley, E.S. and Barnes, M.D. (2013). There's an App for That: Content Analysis of Paid Health & Fitness Apps. Journal of Medical Internet Research, 14(3):e72.
  17. Burton, S.H., Tanner, K.W., Giraud-Carrier, C.G. (2012). Leveraging Social Networks for Anytime-Anyplace Health Information. Network Modeling Analysis in Health Informatics and Bioinformatics, 1(4):173-181.
  18. Burton, S., Tanner, K. and Giraud-Carrier, C. (2012). Leveraging Social Networks for Anytime-Anyplace Health Information. Journal of Network Modeling Analysis in Health Informatics and Bioinformatics, 1(4):173-181.
  19. Burton, S., Morris, R., Hansen, J., Dimond, M., Giraud-Carrier, C., West, J., Hanson, C., Barnes, M. (2012). Public Health Community Mining in YouTube. In Proceedings of the Second ACM International Health Informatics Symposium (IHI 2012), pages 81-90.
  20. Prier, K.W., Smith, M.S., Giraud-Carrier, C. and Hanson, C.L. (2011). Identifying Health-Related Topics in Twitter: An Exploration of Tobacco-Related Tweets as a Test Topic. In Proceedings of the Fourth International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction, 18-25.
  21. Giraud-Carrier, C., Pixton, B. and Rocha, R. (2009). Bariatric Surgery Performance: An Observational Study Using Data Mining Techniques. Journal of Intelligent Data Analysis, 15(3):741-754.
  22. Giraud-Carrier, C. (2008). Improving Clinical Research with Predictive Informatics. Poster at the AMIA Clinical Research Informatics Working Group Expo.
  23. Langford, T., Giraud-Carrier, C. and Magee, J. (2001). Detection of Infectious Outbreaks in Hospitals through Incremental Clustering. In Proceedings of the 8th European Conference on Artificial Intelligence in Medicine (AIME'01), LNAI 2101, 30-39.
  24. Kennedy, C.J., Giraud-Carrier , C. and Bristol, D.W. (1999). Predicting Chemical Carcinogenesis Using Structural Information Only. In Proceedings of the 3rd European Conference on Principles of Data Mining and Knowledge Discovery (PKDD'99), 360-365.

Machine Learning / Data Mining

  1. Han, D., Giraud-Carrier, C. and Shuoru, L. (2015). Efficient Mining of High-speed Uncertain Data Streams. Applied Intelligence, 43(4):773-785.
  2. Giraud-Carrier, C., Goodliffe, J., Jones, B.M. and Cueva, S. (2015). Effective Record Linkage for Mining Campaign Contribution Data. Knowledge and Information Systems, 45(2):389-416.
  3. Gustafson, N. and Giraud-Carrier, C. (2014). A Confidence-Prioritization Approach for Learning Noisy Data. International Journal of Data Analysis Techniques and Strategies, 6(4):307-326.
  4. Burton, S.H. and Giraud-Carrier, C. (2014). Discovering Social Circles in Directed Graphs. ACM Transactions on Knowledge Discovery from Data, 8(4):21.
  5. Burton, S.H, Morris, R.G., Giraud-Carrier, C.G., West, J.H. and Thackeray, R. (2014). Mining Useful Association Rules from Questionnaire Data. Intelligent Data Analysis, 18(3):479-494.
  6. Smith, M.R., Martinez, T. and Giraud-Carrier, C. (2014). An Instance Level Analysis of Data Complexity. Machine Learning, 95(2):225-256.
  7. Lee, J. and Giraud-Carrier, C. (2014). On the Dangers of Default Implementations: The Case of Radial Basis Function Networks. Intelligent Data Analysis, 18(2):261-279.
  8. Tew, C., Giraud-Carrier, C., Burton, S. and Tanner, K. (2014). Behavior-based Clustering and Analysis of Interestingness Measures for Association Rule Mining. Data Mining and Knowledge Discovery, 18(4):1004-1045.
  9. Tanner, K., Giraud-Carrier, C. and Olsen, D.O. (2014). Formatting by Demonstration: An Interactive Machine Learning Approach. International Journal of Computer Applications, 86(18):41-47.
  10. Gustafson, N. and Giraud-Carrier, C. (2013). A Confidence-Prioritization Approach for Learning Noisy Data. International Journal of Data Analysis Techniques and Strategies, in press.
  11. Giraud-Carrier, C. and Dunham, M. (2010). Special Issue on Unexpected Results. SIGKDD Explorations, 12(1).
  12. Heath, D., Zitzelberger, A. and Giraud-Carrier, C. (2010). A Multiple Domain Comparison of Multi-label Classification Methods. In Working Notes of the ICML Workshop on Learning from Multi-Label Data, 21-28.
  13. Taylor, Q. and Giraud-Carrier, C. (2010). Applications of Data Mining in Software Engineering. International Journal of Data Analysis Techniques and Strategies, 2(3):243-257.
  14. Dinerstein, S., Giraud-Carrier, C., Dinerstein, J. and Egbert, P. (2009). Fused Multi-modal Deduplication. Proceedings of the International Conference on Data Mining (DMIN'09), 253-259.
  15. Gashler, M., Giraud-Carrier, C. and Martinez, T. (2008). Decision Tree Ensemble: Small Heterogeneous Is Better Than Large Homogeneous. In Proceedings of the Seventh International Conference on Machine Learning and Applications, 900-905.
  16. Oviatt, D., Frandsen, D., Clements, K. and Giraud-Carrier, C. (2008). An Instance-based Nearest-neighbor Approach to Classifying Nuclear Explosion Data. In Summary Booklet of the Data Mining Contest at the IEEE International Conference on Data Mining, 12-15.
  17. Giraud-Carrier (2008). Data Mining Tool Selection. In Wang, J. (Ed.), Encyclopedia of Data Warehousing and Mining, 2nd Edition, 511-518.
  18. Giraud-Carrier, C. and Smith, M. (2008). Web Mining: Stages of Knowledge Discovery in e-Commerce Sites. In Wang, J. (Ed.), Encyclopedia of Data Warehousing and Mining, 2nd Edition, 1830-1834.
  19. Smith, M., Wenerstrom, B., Giraud-Carrier, C., Lawyer, S. and Liu, W. (2007). Personalizing e-Commerce with Data Mining. In E-Service Intelligence: Methodologies, Technologies and Applications, Lu, J., Ruan, D. and Zhang, G. (Eds.), Studies in Computational Intelligence Series, Vol. 37, Springer, Chapter 12.
  20. Giraud-Carrier, C. and Martinez, T. (2007). Learning by Discrimination: A Constructive Incremental Approach. Journal of Computers, 2(7):49-58.
  21. Tran, N., Giraud-Carrier, C., seppi, K. and Warnick, S. (2006). Cooperation-based Clustering for Profit-maximizing Organizational Design. In Proceedings of the International Joint Conference on Neural Networks (IJCNN'06), 3479-3483. (Contact author for reprint).
  22. Daniels, K. and Giraud-Carrier, C. (2006). Learning the Threshold in Hierarchical Agglomerative Clustering. In Proceedings of the Fifth International Conference on Machine Learning Applications, 270-275.
  23. Wenerstrom, B. and Giraud-Carrier, C. (2006). Temporal Data Mining in Dynamic Feature Spaces. In Proceedings of the Sixth International Conference on Data Mining (ICDM'06), 1141-1145.
  24. Giraud-Carrier, C. and Martinez, T. (2006). A Constructive Incremental Learning Algorithm for Binary Classification Tasks. In Proceedings of the IEEE Mountain Workshop on Adaptive and Learning Systems, 213-218. (Contact author for reprint).
  25. Smith, M. and Giraud-Carrier, C. (2005). Stages of Knowledge Discovery in Websites. In Proceedings of the 1st International Workshop on E-Service Intelligence at the Eighth Joint Conference on Information Sciences, 1585-1588.
  26. Thie, C. and Giraud-Carrier, C. (2005). Learning Concept Descriptions with Typed Evolutionary Programming. IEEE Transactions on Knowledge and Data Engineering, 17(12):1664-1677. (Contact author for reprint).
  27. Povel, O. and Giraud-Carrier, C. (2004). SwissAnalyst: Data Mining Without the Entry Ticket. In Bramer, M. and Devedzic, V. (Eds.), Artificial Intelligence Applications and Innovations (IFIP 18th World Computer Congress, TC12 First International Conference on Artificial Intelligence Applications and Innovations AIAI-2004), Kluwer, 393-406
  28. MacKinney-Romero, R. and Giraud-Carrier, C. (2004). Inducing Classification Rules from Highly-structured Data with Composition. In Proceedings of the 3rd Mexican International Conference on Artificial Intelligence (MICAI'04), LNAI 2972, 262-271.
  29. Giraud-Carrier, C. and Povel, O. (2003). Characterising Data Mining Software. Journal of Intelligent Data Analysis, 7(3):181-192. (Contact author for reprint).
  30. Giraud-Carrier, C. (2002). Unifying Learning and Evolution Through Baldwinian Evolution and Lamarckism: A Case Study. In Advances in Computational Intelligence and Learning: Methods and Applications, Zimmermann, H-J., Tselentis, G., van Someren, M. and Dounias, G. (Eds.), The Kluwer International Series on Intelligent Technologies, 18:159-168.
  31. Giraud-Carrier, C. and Kennedy, C.J. (2001). ADFs: An Evolutionary Approach to Predicate Invention. In Proceedings of the 5th International Conference on Artificial Neural Networks and Genetic Algorithms (ICANNGA'01), 224-227.
  32. Giraud-Carrier, C. (2000). A Note on the Utility of Incremental Learning. AI Communications, 13(4):215-223.
  33. Bowers, A.F., Giraud-Carrier, C. and Lloyd, J.W. (2000). Classification of Individuals with Complex Structure. In Proceedings of the 17th International Conference on Machine Learning, 81-88.
  34. Lock, D. and Giraud-Carrier, C. (1999). Evolutionary Programming of Near-Optimal Neural Networks. In Proceedings of the 4th International Conference on Artificial Neural Networks and Genetic Algorithms (ICANNGA'99), 302-306.
  35. MacKinney-Romero, R. and Giraud-Carrier, C. (1999). Learning from Highly Structured Data by Decomposition. In Proceedings of the 3rd European Conference on Principles of Data Mining and Knowledge Discovery (PKDD'99), 436-441.
  36. Kennedy, C.J. and Giraud-Carrier, C. (1999). A Depth Controlling Strategy for Strongly Typed Evolutionary Programming. In GECCO 1999: Proceedings of the 1st Annual Conference, 879-885.
  37. Kennedy, C.J. and Giraud-Carrier, C. (1999). An Evolutionary Approach to Concept Learning with Structured Data. In Proceedings of the 4th International Conference on Artificial Neural Networks and Genetic Algorithms (ICANNGA'99), 331-336.
  38. Giraud-Carrier, C., Dattani, I. and Corley, S. (1999). Case Base Management through Induction. In Proceedings of the IJCAI'99 Workshop on Automating the Construction of Case Based Reasoners, 44-49.
  39. Bogacz, R., and Giraud-Carrier, C. (1998). BRAINN: A Connectionist Approach to Symbolic Reasoning. In Proceedings of the 1st International ICSC/IFAC Symposium on Neural Computation (NC'98), 907-913.
  40. Giraud-Carrier, C. and Corley, S. (1998). Inductive CBR for Customer Support. In Proceedings of the 2nd International Conference on the Practical Application of Knowledge Discovery and Data Mining (PADD'98), 131-141.
  41. Flach, P.A., Giraud-Carrier, C. and Lloyd, J.W. (1998). Strongly Typed Inductive Concept Learning. In Proceedings of the 8th International Conference on Inductive Logic Programming (ILP-98), LNAI 1446, 185-194.
  42. Bogacz, R. and Giraud-Carrier, C. (1998). Learning Meta-Rules of Selection in Expert Systems. In Proceedings of the 4th World Congress on Expert Systems, 576-581.
  43. Bogacz, R. and Giraud-Carrier, C. (1997). Supervised Competitive Learning for Finding Positions of Radial Basis Functions. In Proceedings of the 3rd Polish Conference on Neural Networks and Their Applications, 701-706.
  44. Burdsall, B. and Giraud-Carrier, C. (1997). GA-RBF: A Self-Optimising RBF Network. In Proceedings of the 3rd International Conference on Artificial Neural Networks and Genetic Algorithms (ICANNGA'97), 348-351.
  45. Burdsall, B. and Giraud-Carrier, C. (1997). Evolving Fuzzy Prototypes for Efficient Data Clustering. In Proceedings of the 2nd International ICSC Symposium on Fuzzy Logic and Applications (ISFL'97), 217-223.
  46. Giraud-Carrier, C. and Ward, M. (1997). Learning Customer Profiles to Generate Cash over the Internet. In Proceedings of the 3rd International Workshop on Applications of Neural Networks to Telecommunications (IWANNT'97), 165-170.
  47. Bowers,A.F., Giraud-Carrier, C., Kennedy,C., Lloyd, J.W. and MacKinney-Romero, R. (1997). A Framework for Higher-Order Inductive Machine Learning. In Proceedings of the COMPULOGNet Area Meeting on Representation Issues in reasoning and Learning, 19-25.
  48. Giraud-Carrier, C. (1996). FLARE: Induction with Prior Knowledge. In Proceedings of the 16th Annual Conference of the British Computer Society Specialist Group on Expert Systems (Expert Systems'96), 11-24.
  49. Giraud-Carrier , C. and Martinez, T. (1995). An Integrated Framework for Learning and Reasoning. Journal of Artificial Intelligence Research, 3(1):147-185.
  50. Giraud-Carrier, C., and Martinez, T. (1995). ILA: Combining Inductive Learning with Prior Knowledge and Reasoning. Technical Report CSTR-95-03, University of Bristol, Department of Computer Science.
  51. Giraud-Carrier, C. and Martinez, T. (1995). AA1*: A Dynamic Incremental Network that Learns by Discrimination. In Proceedings of the 2nd International Conference on Artificial Neural Networks and Genetic Algorithms (ICANNGA'95), 45-48.
  52. Giraud-Carrier, C. and Martinez, T. (1995). Analysis of the Convergence and Generalization of AA1. Journal of Parallel and Distributed Computing, 26(1):125-131.
  53. Giraud-Carrier, C. and Martinez, T. (1994). Seven Desirable Properties for Artificial Learning Systems. In Proceedings of the 7th Florida AI Research Symposium (FLAIRS'94), 16-20.
  54. Giraud-Carrier, C. and Martinez, T. (1994). An Incremental Learning Model for Commonsense Reasoning. In Proceedings of the 7th International Symposium on Artificial Intelligence (ISAI'94), 134-141.
  55. Giraud-Carrier, C. and Martinez, T. (1993). Using Precepts to Augment Training Set Learning. In Proceedings of the 1st New Zealand International Two-Stream Conference on Artificial Neural Networks and Expert systems (ANNES'93), 46-51.
  56. Martinez, T., Barker, C. and Giraud-Carrier, C. (1993). A Generalizing Adaptive Discriminant Network. In Proceedings of the 1st World Congress on Neural Networks (WCNN'93), Vol. 1, 613-616.

Computational Social Science

  1. Stirling, W., Giraud-Carrier, C. and Felin, T. (2012). A Framework for the Design and Synthesis of Coordinated Social Systems. In Proceedings of the Fourth International Conference on Social Informatics (LNCS 7710), 351-364.
  2. Smith, M., Giraud-Carrier, C., Dewey, D., Ring, S. and Gore, D. (2011). Social Capital and Language Acquisition during Study Abroad. In Proceedings of the Thirty-Third Annual Conference of the Cognitive Science Society.
  3. Smith, M. and Giraud-Carrier, C. (2010). Bonding vs. Bridging Social Capital: A Case Study in Twitter. In Proceedings of the Second International Symposium on Social Intelligence and Networking, 385-392.
  4. Goodliffe, J., Jones, B., Magleby, D.B., Olsen, J.A., Giraud-Carrier, C., Huang, Y., Rowley, W. and Wilcox, D. (2009). Using Record Linkage to Study Campaign Contributors. Poster at the Political Methodology Conference.
  5. Smith, M., Giraud-Carrier, C. and Purser, N. (2009). Implicit Affinity Networks and Social Capital. Journal of Information Technology and Management, 10(2-3):123-134.
  6. Smith, M., Purser, N. and Giraud-Carrier, C. (2008). Social Capital in the Blogosphere: A Case Study. In Papers from the AAAI Spring Symposium on Social Information Processing, K. Lerman et al. (Eds.), Technical Report SS-08-06, AAAI Press, 93-97.
  7. Smith, M., Giraud-Carrier, C. and Judkins, B. (2007). Implicit Affinity Networks. In Proceedings of the Seventeenth Annual Workshop on Information Technologies and Systems, 1-6.

Semantic Distance

  1. Davis, N., Giraud-Carrier, C. and Jensen, D. (2010). A Topological Embedding of the Lexicon for Semantic Distance Computation. Natural Language Engineering, 16(3):245-275.
  2. Jensen, D., Giraud-Carrier, C. and Davis, N. (2008). A Method for Computing Lexical Semantic Distance Using Linear Functionals. Journal of Web Semantics: Science, Services and Agents on the World Wide Web, 6:99-108.
  3. Jensen, D. and Giraud-Carrier, C. (2007). A Topological Embedding of the Lexicon for Effective Semantic Distance Computation. In Proceedings of the Seventh International Workshop on Computational Semantics, 259-270.

Family History

  1. Valentine, D., Mortorff, D. and Giraud-Carrier, C. (2009). Implementing a Surname Study Website with Drupal. Journal of One-Name Studies, 10(4):21-23.
  2. Valentine, D., Mortorff, D. and Giraud-Carrier, C. (2009). Implementing a Surname Study Website with Drupal. In Proceedings of the 9th Annual Workshop on Technology for Family History and Genealogical Research, 88-95.
  3. Ivie, S., Pixton, B. and Giraud-Carrier, C. (2007). Metric-Based Data Mining Model for Genealogical Record Linkage. In Proceedings of the IEEE International Conference on Information Reuse and Integration, 538-543.
  4. Ivie, S., Henry, G., Gatrell, H. and Giraud-Carrier, C. (2007). A Metric-Based Machine Learning Approach to Genealogical Record Linkage. In Proceedings of the 7th Annual Workshop on Technology for Family History and Genealogical Research.
  5. Smith, M. and Giraud-Carrier, C. (2006). Genealogical Implicit Affinity Networks. In Proceedings of the 6th Annual Workshop on Technology for Family History and Genealogical Research.
  6. Pixton, B. and Giraud-Carrier, C. (2006). Using Structured Neural Networks for Record Linkage. In Proceedings of the 6th Annual Workshop on Technology for Family History and Genealogical Research.
  7. Pixton, B. and Giraud-Carrier, C. (2005). MAL4:6 - Using Data Mining for Record Linkage. In Proceedings of the 5th Annual Workshop on Technology for Family History and Genealogical Research.

Adaptive Behavior

  1. Dahl, T.S. and Giraud-Carrier, C. (2005). Incremental Development of Adaptive Behaviors using Trees of Self-Contained Solutions. Adaptive Behavior, 13(3):243-260. (Contact author for reprint).
  2. Dahl, T. and Giraud-Carrier, C. (2004). Evolution-inspired Incremental Development of Complex Autonomous Intelligence. In Proceedings of the 8th International Conference on Intelligent Autonomous Systems (IAS'04), 395-402.
  3. Dahl, T. and Giraud-Carrier, C. (2001). Evolution, Adaption and Behavioural Holism in Artificial Intelligence. In Proceedings of the 6th European Conference on Artificial Life (ECAL'01), LNAI 2159, 499-508. (Please contact auhtor for reprint).
  4. Dahl, T. and Giraud-Carrier, C. (2001). PLANCS: Classes for Programming Adaptive Behaviour Based Robots. In Proceedings of the AISB'01 Symposium on Nonconscious Intelligence: From Natural to Artificial, 9-20.

Computational Neuroscience

  1. Bogacz, R., Brown, M.W. and Giraud-Carrier, C. (2001). Emergence of Motion-sensitive Neurons Properties by Learning Sparse Code for Natural Moving Images. In Advances in Neural Information Processing Systems, Leen, T.K., Dietterich, T.G. and Tresp, V. (Eds.), 13:838-844.
  2. Bogacz, R., Brown, M.W. and Giraud-Carrier, C. (2001). Model of Familiarity Discrimination in the Perirhinal Cortex. Journal of Computational Neuroscience, 10(1):5-23.
  3. Bogacz, R., Brown, M.W. and Giraud-Carrier, C. (2001). A Familiarity Discrimination Algorithm Inspired by Computations of the Perirhinal Cortex. In Emergent Neural Computational Architectures based on Neuroscience, Wermter, S., Austin, J. and Willshaw, D. (Eds.), 435-448.
  4. Bogacz, R., Brown, M.W. and Giraud-Carrier, C. (2001). Model of Co-operation between Recency, Familiarity and Novelty Neurons in the Perirhinal Cortex. In Neurocomputing (Proceedings of CNS-2000), 38:1121-1126.
  5. Bogacz, R., Brown, M.W. and Giraud-Carrier, C. (2000). Frequency-based Error Back-propagation in a Cortical Network. In Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, Vol. II, 211-216.
  6. Bogacz, R. and Giraud-Carrier., C. (2000). A Novel Modular Neural Architecture for Rule-based and Similarity-based Reasoning. In Hybrid Neural Systems, LNAI 1778, Wermter, S. and Sun, R. (Eds.), 63-77.
  7. Bogacz, R., Brown, M.W. and Giraud-Carrier, C. (1999). High Capacity Neural Networks for Familiarity Discrimination. In Proceedings of the 9h International Conference on Artificial Neural Networks (ICANN'99), Vol. 2, 773-778.
  8. Bogacz, R., Brown, M.W. and Giraud-Carrier, C. (1999). Model of Familiarity Discrimination in the Brain: Efficiency, Speed and Robustness. In Proceedings of EmerNet: International Workshop on Emergent Neural Computational Architectures Based on Neuroscience, 23-26.

Software Engineering

  1. Delorey, D., Knutson, C. and Giraud-Carrier, C. (2007). Programming Language Trends in Open Source Development: An Evaluation Using Data from All Production Phase SourceForge Projects. In Proceedings of the Second International Workshop on Public Data about Software Development (see WoPDaSD2007 Program).
  2. Giraud-Carrier, C. (2005). Effective Object-Oriented Behaviour Modelling with State Nets. Manuscript (Upgrade of Technical Report BYU-CS-91-5).
  3. Giraud-Carrier, C. (1994). A Reconfigurable Data Flow Machine for Implementing Functional Programming Languages. SIGPLAN Notices, 29(9):22-28.
  4. Giraud-Carrier, C., Woodfield, S.N. and Embley, D.W. (1993). State Nets: An Expressively Efficient Behavioral Model. In Proceedings of the 12th Annual IEEE International Phoenix Conference on Computers and Communication (IPCCC'93), 571-577.
  5. Embley, D.W., Clyde, S.W., Giraud-Carrier, C., and Woodfield S.N. (1991). A Formal Definition of OSA. Technical Report BYU-CS-91-6, Brigham Young University, Department of Computer Science.
  6. Giraud-Carrier, C. (1991). State Nets are Turing-Equivalent. Technical Report BYU-CS-91-5, Brigham Young University, Department of Computer Science.

Miscellaneous

  1. Hawkins, B. and Giraud-Carrier, C. (2009). Ranking Search Results for Translated Content. In Proceedings of the IEEE International Conference on Information Reuse and Integration, 242-245.
  2. Tran, N., West, D., Giraud-Carrier, C., Seppi, K., Warnick, S. and Johnson, R. (2005). The Value of Cooperation Within a Profit-Maximizing Organization. In Proceedings of the 4th International Conference on Computational Intelligence in Economics and Finance at the Eighth Joint Conference on Information Sciences, 1017-1020.
  3. Fall, C.J. and Giraud-Carrier, C. (2005). Searching Trademark Databases for Verbal Similarities. World Patent Information, 27(2):135-143.
  4. Giraud-Carrier, C., Miclo, P., Daudin, H. and Du Pasquier, J. (2004). An Extranet Waste Inventory Application. In Proceedings of the 18th International Conference on Informatics for Environmental Protection (EnviroInfo Symposium 2004), 37-47.
  5. Smyth, S., Dattani, and Giraud-Carrier, C. (1999). Enhancing Non-destructive Testing with Case-based Reasoning. Accepted for presentation at the IASTED International Conference on Artificial Intelligence and Soft Computing. (Published as: Smyth, S., Enhancing Non-Destructive Testing with Case Based Reasoning, PR-98-06, U

Professional Journals

  1. Your top line: Avoid ranking products only by revenue generated. Utah CEO Magazine, p. 54, July 2008.
  2. Das Vertrauen eines Kunden gewinnen kann Jahre dauern, es verlieren nur einen Augenblick! Vom operativen zum analytischen CRM. IT Business, 2(55), 2001.