{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T11:52:30Z","timestamp":1726055550560},"publisher-location":"Cham","reference-count":18,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030218096"},{"type":"electronic","value":"9783030218102"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-21810-2_14","type":"book-chapter","created":{"date-parts":[[2019,10,23]],"date-time":"2019-10-23T00:26:46Z","timestamp":1571790406000},"page":"359-372","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Markov Blanket Ranking Using Kernel-Based Conditional Dependence Measures"],"prefix":"10.1007","author":[{"given":"Eric V.","family":"Strobl","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shyam","family":"Visweswaran","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,10,23]]},"reference":[{"unstructured":"Constantin F Aliferis, Ioannis Tsamardinos, and Alexander Statnikov. Hiton: a novel markov blanket algorithm for optimal variable selection. In AMIA Annual Symposium Proceedings, volume 2003, page 21. American Medical Informatics Association, 2003.","key":"14_CR1"},{"issue":"4","key":"14_CR2","doi-asserted-by":"publisher","first-page":"1871","DOI":"10.1214\/08-AOS637","volume":"37","author":"Kenji Fukumizu","year":"2009","unstructured":"Kenji Fukumizu, Francis R Bach, Michael I Jordan, et al. Kernel dimension reduction in regression. The Annals of Statistics, 37(4):1871\u20131905, 2009.","journal-title":"The Annals of Statistics"},{"issue":"Dec","key":"14_CR3","first-page":"2075","volume":"6","author":"Arthur Gretton","year":"2005","unstructured":"Arthur Gretton, Ralf Herbrich, Alexander Smola, Olivier Bousquet, and Bernhard Sch\u00f6lkopf. Kernel methods for measuring independence. Journal of Machine Learning Research, 6(Dec):2075\u20132129, 2005.","journal-title":"Journal of Machine Learning Research"},{"doi-asserted-by":"crossref","unstructured":"Isabelle Guyon, Constantin Aliferis, and Andr\u00e9 Elisseeff. Causal feature selection. In Computational methods of feature selection, pages 75\u201397. Chapman and Hall\/CRC, 2007.","key":"14_CR4","DOI":"10.1201\/9781584888796.ch4"},{"unstructured":"Patrik O Hoyer, Dominik Janzing, Joris M Mooij, Jonas Peters, and Bernhard Sch\u00f6lkopf. Nonlinear causal discovery with additive noise models. In Advances in neural information processing systems, pages 689\u2013696, 2009.","key":"14_CR5"},{"key":"14_CR6","volume-title":"Probabilistic graphical models: principles and techniques","author":"Daphne Koller","year":"2009","unstructured":"Daphne Koller, Nir Friedman, and Francis Bach. Probabilistic graphical models: principles and techniques. MIT Press, 2009."},{"unstructured":"Qiang Lou and Zoran Obradovic. Feature selection by approximating the markov blanket in a kernel-induced space. In ECAI:European Conference on Artificial Intelligence, pages 797\u2013802, 2010.","key":"14_CR7"},{"unstructured":"Subramani Mani and Gregory F Cooper. A study in causal discovery from population-based infant birth and death records. In Proceedings of the AMIA Symposium, page 315. American Medical Informatics Association, 1999.","key":"14_CR8"},{"doi-asserted-by":"crossref","unstructured":"Jose M Pe\u00f1a, Johan Bj\u00f6rkegren, and Jesper Tegn\u00e9r. Scalable, efficient and correct learning of markov boundaries under the faithfulness assumption. In European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty, pages 136\u2013147. Springer, 2005.","key":"14_CR9","DOI":"10.1007\/11518655_13"},{"issue":"5721","key":"14_CR10","doi-asserted-by":"publisher","first-page":"523","DOI":"10.1126\/science.1105809","volume":"308","author":"K. Sachs","year":"2005","unstructured":"Karen Sachs, Omar Perez, Dana Pe\u2019er, Douglas A Lauffenburger, and Garry P Nolan. Causal protein-signaling networks derived from multiparameter single-cell data. Science, 308(5721):523\u2013529, 2005.","journal-title":"Science"},{"unstructured":"Shohei Shimizu, Patrik O Hoyer, Aapo Hyv\u00e4rinen, and Antti Kerminen. A linear non-gaussian acyclic model for causal discovery. Journal of Machine Learning Research, 7(Oct):2003\u20132030, 2006.","key":"14_CR11"},{"issue":"13","key":"14_CR12","doi-asserted-by":"publisher","first-page":"i490","DOI":"10.1093\/bioinformatics\/btm216","volume":"23","author":"Le Song","year":"2007","unstructured":"Le Song, Justin Bedo, Karsten M Borgwardt, Arthur Gretton, and Alex Smola. Gene selection via the bahsic family of algorithms. Bioinformatics, 23(13):i490\u2013i498, 2007.","journal-title":"Bioinformatics"},{"unstructured":"Peter Spirtes. An anytime algorithm for causal inference. In AISTATS: Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics, 2001.","key":"14_CR13"},{"doi-asserted-by":"crossref","unstructured":"Peter Spirtes, Clark N Glymour, Richard Scheines, David Heckerman, Christopher Meek, Gregory Cooper, and Thomas Richardson. Causation, prediction, and search. MIT Press, 2000.","key":"14_CR14","DOI":"10.7551\/mitpress\/1754.001.0001"},{"unstructured":"Alexander Statnikov, Nikita I Lytkin, Jan Lemeire, and Constantin F Aliferis. Algorithms for discovery of multiple markov boundaries. Journal of Machine Learning Research, 14(Feb):499\u2013566, 2013.","key":"14_CR15"},{"unstructured":"Ioannis Tsamardinos and Constantin F Aliferis. Towards principled feature selection: relevancy, filters and wrappers. In AISTATS: Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, 2003.","key":"14_CR16"},{"issue":"1","key":"14_CR17","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1007\/s10994-006-6889-7","volume":"65","author":"Ioannis Tsamardinos","year":"2006","unstructured":"Ioannis Tsamardinos, Laura E Brown, and Constantin F Aliferis. The max-min hill-climbing bayesian network structure learning algorithm. Machine learning, 65(1):31\u201378, 2006.","journal-title":"Machine Learning"},{"unstructured":"Kun Zhang, Jonas Peters, Dominik Janzing, and Bernhard Sch\u00f6lkopf. Kernel-based conditional independence test and application in causal discovery. In Proceedings of the Twenty-Seventh Conference on Uncertainty in Artificial Intelligence, pages 804\u2013813. AUAI Press, 2011.","key":"14_CR18"}],"container-title":["The Springer Series on Challenges in Machine Learning","Cause Effect Pairs in Machine Learning"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-21810-2_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,10,23]],"date-time":"2019-10-23T00:41:23Z","timestamp":1571791283000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-21810-2_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030218096","9783030218102"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-21810-2_14","relation":{},"ISSN":["2520-131X","2520-1328"],"issn-type":[{"type":"print","value":"2520-131X"},{"type":"electronic","value":"2520-1328"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"23 October 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}