An iterative learning algorithm for within-network regression in the transductive setting

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5808 LNAI, Issue , 2009, Pages 36-50

  Appice, Annalisa ^a   Ceci, Michelangelo ^a   Malerba, Donato ^a  

^a UNIVERSITY OF BARI   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

ATTRIBUTE VALUES; CO-TRAINING; COMPUTATIONAL SOLUTIONS; DATA SETS; INDEPENDENCE ASSUMPTION; ITERATIVE LEARNING; ITERATIVE LEARNING PROCESS; LABELED DATA; MODEL TREES; NEIGHBORHOOD RELATION; PREDICTOR VARIABLES; REGRESSION MODEL;

CORRELATION DETECTORS; DIELECTRIC RELAXATION; LEARNING ALGORITHMS; LEARNING SYSTEMS; REGRESSION ANALYSIS;

LABELS;

ALGORITHMS; CORRELATION; DETECTORS; LABELS; REGRESSION ANALYSIS;

EID: 71049140540     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-04747-3_6     Document Type: Conference Paper

References (23)

1. Abreu, M., de Groot, H., Florax, R.: Space and growth: A survey of empirical evidence and methods. Region and Development, 12-43 (2005)
2. Anselin, L.: Spatial externalities, spatial multipliers and spatial econometrics. International Regional Science Review (26), 153-166 (2003)
3. Appice, A., Dzeroski, S.: Stepwise induction of multi-target model trees. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladeni?c, D., Skowron, A. (eds.) ECML 2007. LNCS (LNAI), vol.4701, pp. 502-509. Springer, Heidelberg (2007)
4. Blum, A., Mitchell, T.M.: Combining labeled and unlabeled data with co-training. In: COLT, pp. 92-100 (1998)
5. Brefeld, U., Gärtner, T., Scheffer, T., Wrobel, S.: Efficient co-regularised least squares regression. In: Cohen, W.W., Moore, A. (eds.) 23th International Conference on Machine Learning, ICML 2006. ACM International Conference Proceeding Series, vol.148, pp. 137-144. ACM, New York (2006)
6. Charlton, M., Fotheringham, S., Brunsdon, C.: Geographically weighted regression. In: ESRC National Centre for Research Methods NCRM Methods Review Papers NCRM/006 (2005)
7. Cortez, P., Morais, A.: A data mining approach to predict forest fires using meteorological data, pp. 512-523. APPIA (2007)
8. Demšar, D., Debeljak, M., Lavigne, C., Džeroski, S.: Modelling pollen dispersal of genetically modified oilseed rape within the field. In: Abstracts of the 90th ESA Annual Meeting, The Ecological Society of America, p. 152 (2005)
9. Gallagher, B., Tong, H., Eliassi-Rad, T., Faloutsos, C.: Using ghost edges for classification in sparsely labeled networks. In: Proceeding of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2008, pp. 256-264. ACM, New York (2008)
10. David, J., Jennifer, N., Brian, G.: Why collective inference improves relational classification. In: Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2004, pp. 593-598. ACM, New York (2004)
11. Macskassy, S.A., Provost, F.: A Brief Survey of Machine Learning Methods for Classification in Networked Data and an Application to Suspicion Scoring. In: Airoldi, E.M., Blei, D.M., Fienberg, S.E., Goldenberg, A., Xing, E.P., Zheng, A.X. (eds.) ICML 2006. LNCS, vol.4503, pp. 172-175. Springer, Heidelberg (2007)
12. Macskassy, S., Provost, F.: Classification in networked data: a toolkit and a univariate case study. Machine Learning 8, 935-983 (2007)
13. Macskassy, S.A.: Improving learning in networked data by combining explicit and mined links. In: Proceedings of the 22nd Conference on Artificial Intelligence, AAAI 2007, pp. 590-595. AAAI Press, Menlo Park (2007)
14. McPherson, M., Smith-Lovin, L., Cook, J.: Birds of a feather: Homophily in social networks. Annual Review of Sociology 27, 415-444 (2001)
15. Jennifer, N., David, J.: Relational dependency networks. Journal of Machine Learning Research 8, 653-692 (2007)
16. Neville, J., Simsek, O., Jensen, D.: Autocorrelation and relational learning: Challenges and opportunities. In: Proceedings of the Workshop on Statistical Relational Learning (2004)
17. Pace, P., Barry, R.: Quick computation of regression with a spatially autoregressive dependent variable. Geographical Analysis 29(3), 232-247 (1997)
18. Rey, S.J., Montouri, B.D.: U.s. regional income convergence: a spatial econometric perspective. Regional Studies (33), 145-156 (1999)
19. Sen, P., Namata, G., Bilgic, M., Getoor, L., Gallagher, B., Eliassi-Rad, T.: Collective classification in network data. AI Magazine 29(3), 93-106 (2008)
20. Tobler, W.: Cellular geography. In: Gale, S., Olsson, G. (eds.) Philosophy in Geography (1979)
21. Vapnik, V.: Statistical Learning Theory. Wiley, New York (1998)
22. Zhou, Z.-H., Li, M.: Semisupervised regression with cotraining-style algorithms. IEEE Transaction in Knowledge Data Engineering 19(11), 1479-1493 (2007)
23. Zhu, X., Ghahramani, Z., Lafferty, J.D.: Semi-supervised learning using gaussian fields and harmonic functions. In: Fawcett, T., Mishra, N. (eds.) Proceedings of the 20th International Conference on Machine Learning, ICML 2003, pp. 912-919. AAAI Press, Menlo Park (2003)