{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T08:50:09Z","timestamp":1742979009003,"version":"3.40.3"},"publisher-location":"Berlin, Heidelberg","reference-count":26,"publisher":"Springer Berlin Heidelberg","isbn-type":[{"type":"print","value":"9783642238079"},{"type":"electronic","value":"9783642238086"}],"license":[{"start":{"date-parts":[[2011,1,1]],"date-time":"2011-01-01T00:00:00Z","timestamp":1293840000000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2011]]},"DOI":"10.1007\/978-3-642-23808-6_22","type":"book-chapter","created":{"date-parts":[[2011,8,18]],"date-time":"2011-08-18T03:40:29Z","timestamp":1313638829000},"page":"333-348","source":"Crossref","is-referenced-by-count":9,"title":["Network Regression with Predictive Clustering Trees"],"prefix":"10.1007","author":[{"given":"Daniela","family":"Stojanova","sequence":"first","affiliation":[]},{"given":"Michelangelo","family":"Ceci","sequence":"additional","affiliation":[]},{"given":"Annalisa","family":"Appice","sequence":"additional","affiliation":[]},{"given":"Sa\u0161o","family":"D\u017eeroski","sequence":"additional","affiliation":[]}],"member":"297","reference":[{"key":"22_CR1","doi-asserted-by":"crossref","unstructured":"Angin, P., Neville, J.: A shrinkage approach for modeling non-stationary relational autocorrelation. In: Proc. 8th IEEE Intl. Conf. on Data Mining, pp. 707\u2013712 (2008)","DOI":"10.1109\/ICDM.2008.147"},{"key":"22_CR2","doi-asserted-by":"crossref","unstructured":"Appice, A., Ceci, M., Malerba, D.: An iterative learning algorithm for within-network regression in the transductive setting. In: Discovery Science, pp. 36\u201350 (2009)","DOI":"10.1007\/978-3-642-04747-3_6"},{"key":"22_CR3","unstructured":"Blockeel, H., De Raedt, L., Ramon, J.: Top-down induction of clustering trees. In: Proc. 15th Intl. Conf. on Machine Learning, pp. 55\u201363 (1998)"},{"key":"22_CR4","volume-title":"Classification and Regression trees","author":"L. Breiman","year":"1984","unstructured":"Breiman, L., Friedman, J., Olshen, R., Stone, J.: Classification and Regression trees. Wadsworth & Brooks, Belmont (1984)"},{"key":"22_CR5","volume-title":"Algorithms for Minimization without Derivatives","author":"R. Brent","year":"1973","unstructured":"Brent, R.: Algorithms for Minimization without Derivatives. Prentice-Hall, Englewood Cliffs (1973)"},{"key":"22_CR6","unstructured":"Cortez, P., Morais, A.: A Data Mining Approach to Predict Forest Fires using Meteorological Data. In: Proc. 13th Portuguese Conf. Artificial Intelligence, New Trends in Artificial Intelligence, pp. 512\u2013523 (2007)"},{"key":"22_CR7","unstructured":"Dem\u0161ar, D., Debeljak, M., Lavigne, C., D\u017eeroski S.: Modelling pollen dispersal of genetically modified oilseed rape within the field. In: Abstracts of the 90th ESA Annual Meeting, p. 152. The Ecological Society of America (2005)"},{"key":"22_CR8","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1007\/978-3-540-75549-4_5","volume-title":"Proc. 5th Intl. Wshp. on Knowledge Discovery in Inductive Databases","author":"S. D\u017eeroski","year":"2007","unstructured":"D\u017eeroski, S., Gjorgjioski, V., Slavkov, I., Struyf, J.: Analysis of time series data with predictive clustering trees. In: Proc. 5th Intl. Wshp. on Knowledge Discovery in Inductive Databases, pp. 63\u201380. Springer, Heidelberg (2007)"},{"key":"22_CR9","volume-title":"Geographically Weighted Regression: The Analysis of Spatially Varying Relationships","author":"A.S. Fotheringham","year":"2002","unstructured":"Fotheringham, A.S., Brunsdon, C., Charlton, M.: Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley, Chichester (2002)"},{"key":"22_CR10","doi-asserted-by":"crossref","unstructured":"Gallagher, B., Tong, H., Eliassi-Rad, T., Faloutsos, C.: Using ghost edges for classification in sparsely labeled networks. In: Proc. 14th ACM SIGKDD Intl. Conf. Knowledge Discovery and Data Mining, pp. 256\u2013264 (2008)","DOI":"10.1145\/1401890.1401925"},{"key":"22_CR11","series-title":"Lecture Notes in Artificial Intelligence","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1007\/3-540-36755-1_10","volume-title":"Machine Learning: ECML 2002","author":"G. G\u00f3ra","year":"2002","unstructured":"G\u00f3ra, G., Wojna, A.: RIONA: A classifier combining rule induction and k-NN method with automated selection of optimal neighbourhood. In: Elomaa, T., Mannila, H., Toivonen, H. (eds.) ECML 2002. LNCS (LNAI), vol.\u00a02430, pp. 111\u2013123. Springer, Heidelberg (2002)"},{"key":"22_CR12","doi-asserted-by":"crossref","unstructured":"Jensen, D., Neville, J., Gallagher, B.: Why collective inference improves relational classification. In: Proc. 10th Intl. Conf. on Knowledge Discovery and Data Mining, pp. 593\u2013598 (2004)","DOI":"10.1145\/1014052.1014125"},{"issue":"6","key":"22_CR13","doi-asserted-by":"publisher","first-page":"1659","DOI":"10.2307\/1939924","volume":"74","author":"P. Legendre","year":"1993","unstructured":"Legendre, P.: Spatial autocorrelation: Trouble or new paradigm? Ecology\u00a074(6), 1659\u20131673 (1993)","journal-title":"Ecology"},{"key":"22_CR14","first-page":"935","volume":"8","author":"S. Macskassy","year":"2007","unstructured":"Macskassy, S., Provost, F.: Classification in networked data: a toolkit and a univariate case study. Machine Learning\u00a08, 935\u2013983 (2007)","journal-title":"Machine Learning"},{"key":"22_CR15","unstructured":"Macskassy, S.A.: Improving learning in networked data by combining explicit and mined links. In: Proc. 22th Intl. Conf. on Artificial Intelligence, pp. 590\u2013595 (2007)"},{"key":"22_CR16","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1146\/annurev.soc.27.1.415","volume":"27","author":"M. McPherson","year":"2001","unstructured":"McPherson, M., Smith-Lovin, L., Cook, J.: Birds of a feather: Homophily in social networks. Annual Review of Sociology\u00a027, 415\u2013444 (2001)","journal-title":"Annual Review of Sociology"},{"key":"22_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1007\/BFb0014141","volume-title":"Advances in Database Technology EDBT \u201996","author":"M. Mehta","year":"1996","unstructured":"Mehta, M., Agrawal, R., Rissanen, J.: Sliq: A fast scalable classifier for data mining. In: Apers, P.M.G., Bouzeghoub, M., Gardarin, G. (eds.) EDBT 1996. LNCS, vol.\u00a01057, pp. 18\u201332. Springer, Heidelberg (1996)"},{"key":"22_CR18","doi-asserted-by":"crossref","unstructured":"Michalski, R.S., Stepp, R.: Machine Learning: An Artificial Intelligence Approach. In: Learning from Observation: Conceptual Clustering, Tioga, pp. 331\u2013363 (2003)","DOI":"10.1007\/978-3-662-12405-5_11"},{"key":"22_CR19","first-page":"653","volume":"8","author":"J. Neville","year":"2007","unstructured":"Neville, J., Jensen, D.: Relational dependency networks. Journal of Machine Learning Research\u00a08, 653\u2013692 (2007)","journal-title":"Journal of Machine Learning Research"},{"key":"22_CR20","doi-asserted-by":"crossref","unstructured":"Neville, J., Simsek, O., Jensen, D.: Autocorrelation and relational learning: Challenges and opportunities. In: Wshp. Statistical Relational Learning (2004)","DOI":"10.21236\/ADA472226"},{"key":"22_CR21","volume-title":"Vital Statistics","author":"M. Orkin","year":"1990","unstructured":"Orkin, M., Drogin, R.: Vital Statistics. McGraw Hill, New York (1990)"},{"issue":"3","key":"22_CR22","doi-asserted-by":"publisher","first-page":"232","DOI":"10.1111\/j.1538-4632.1997.tb00959.x","volume":"29","author":"P. Pace","year":"1997","unstructured":"Pace, P., Barry, R.: Quick computation of regression with a spatially autoregressive dependent variable. Geographical Analysis\u00a029(3), 232\u2013247 (1997)","journal-title":"Geographical Analysis"},{"key":"22_CR23","doi-asserted-by":"crossref","unstructured":"Randic, M.: On characterization of molecular attributes. Journal of American Chemical Society (1975)","DOI":"10.1021\/ja00856a001"},{"issue":"3","key":"22_CR24","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1609\/aimag.v29i3.2157","volume":"29","author":"P. Sen","year":"2008","unstructured":"Sen, P., Namata, G., Bilgic, M., Getoor, L., Gallagher, B., Eliassi-Rad, T.: Collective classification in network data. AI Magazine\u00a029(3), 93\u2013106 (2008)","journal-title":"AI Magazine"},{"key":"22_CR25","volume-title":"Statistical Learning Theory","author":"V. Vapnik","year":"1998","unstructured":"Vapnik, V.: Statistical Learning Theory. Wiley, New York (1998)"},{"key":"22_CR26","unstructured":"Zhu, X., Ghahramani, Z., Lafferty, J.D.: Semi-supervised learning using gaussian fields and harmonic functions. In: Proc. 20th Intl. Conf. on Machine Learning, pp. 912\u2013919 (2003)"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-642-23808-6_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,6,14]],"date-time":"2019-06-14T00:47:46Z","timestamp":1560473266000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-642-23808-6_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2011]]},"ISBN":["9783642238079","9783642238086"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-642-23808-6_22","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2011]]}}}