{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,4]],"date-time":"2025-02-04T18:10:01Z","timestamp":1738692601729,"version":"3.37.0"},"publisher-location":"Berlin, Heidelberg","reference-count":20,"publisher":"Springer Berlin Heidelberg","isbn-type":[{"type":"print","value":"9783540893776"},{"type":"electronic","value":"9783540893783"}],"license":[{"start":{"date-parts":[[2008,1,1]],"date-time":"2008-01-01T00:00:00Z","timestamp":1199145600000},"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":[[2008]]},"DOI":"10.1007\/978-3-540-89378-3_31","type":"book-chapter","created":{"date-parts":[[2008,11,26]],"date-time":"2008-11-26T21:43:58Z","timestamp":1227735838000},"page":"318-324","source":"Crossref","is-referenced-by-count":0,"title":["L1 LASSO Modeling and Its Bayesian Inference"],"prefix":"10.1007","author":[{"given":"Junbin","family":"Gao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael","family":"Antolovich","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Paul W.","family":"Kwan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","reference":[{"key":"31_CR1","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","volume":"58","author":"R. Tibshirani","year":"1996","unstructured":"Tibshirani, R.: Regression shrinkage and selection via the LASSO. J. Royal. Statist. Soc. B\u00a058, 267\u2013288 (1996)","journal-title":"J. Royal. Statist. Soc. B"},{"issue":"1","key":"31_CR2","doi-asserted-by":"publisher","first-page":"374","DOI":"10.1016\/j.csda.2006.12.019","volume":"52","author":"N. Meinshausen","year":"2007","unstructured":"Meinshausen, N.: Relaxed LASSO. Computational Statistics & Data Analysis\u00a052(1), 374\u2013393 (2007)","journal-title":"Computational Statistics & Data Analysis"},{"issue":"4","key":"31_CR3","doi-asserted-by":"publisher","first-page":"586","DOI":"10.1109\/JSTSP.2007.910281","volume":"1","author":"M. Figueiredo","year":"2007","unstructured":"Figueiredo, M., Nowak, R., Wright, S.: Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems. IEEE Journal of Selected Topics in Signal Processing\u00a01(4), 586\u2013597 (2007)","journal-title":"IEEE Journal of Selected Topics in Signal Processing"},{"key":"31_CR4","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1214\/08-SS035","volume":"2","author":"T. Hesterberg","year":"2008","unstructured":"Hesterberg, T., Choi, N., Meier, L., Fraley, C.: Least angle and L1 regression: A Review. Statistics Surveys\u00a02, 61\u201393 (2008)","journal-title":"Statistics Surveys"},{"key":"31_CR5","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1214\/009053604000000067","volume":"32","author":"B. Efron","year":"2004","unstructured":"Efron, B., Hastie, T., Johnstone, I., Tibshirani, R.: Least angle regression. Annals of Statistics\u00a032, 407\u2013451 (2004)","journal-title":"Annals of Statistics"},{"key":"31_CR6","volume-title":"Statistical Learning Theory","author":"V. Vapnik","year":"1998","unstructured":"Vapnik, V.: Statistical Learning Theory. Wiley, New York (1998)"},{"key":"31_CR7","volume-title":"Learning with Kernels","author":"B. Sch\u00f6lkopf","year":"2002","unstructured":"Sch\u00f6lkopf, B., Smola, A.: Learning with Kernels. The MIT Press, Cambridge (2002)"},{"key":"31_CR8","doi-asserted-by":"publisher","first-page":"559","DOI":"10.1016\/j.neucom.2004.12.011","volume":"69","author":"S. Chen","year":"2006","unstructured":"Chen, S.: Local regularization assisted orthogonal least squares regression. NeuroComputing\u00a069, 559\u2013585 (2006)","journal-title":"NeuroComputing"},{"key":"31_CR9","doi-asserted-by":"crossref","unstructured":"Drezet, P., Harrison, R.: Support vector machines for system identification. In: Proceeding of UKACC Int. Conf. Control 1998, Swansea, U.K., pp. 688\u2013692 (1998)","DOI":"10.1049\/cp:19980312"},{"key":"31_CR10","first-page":"211","volume":"1","author":"M. Tipping","year":"2001","unstructured":"Tipping, M.: Sparse Bayesian learning and the relevance vector machine. J. Machine Learning Research\u00a01, 211\u2013244 (2001)","journal-title":"J. Machine Learning Research"},{"issue":"5","key":"31_CR11","doi-asserted-by":"publisher","first-page":"1873","DOI":"10.1080\/00207178908953472","volume":"50","author":"S. Chen","year":"1989","unstructured":"Chen, S., Billings, S., Luo, W.: Orthogonal least squares methods and their application to non-linear system identification. International Journal of Control\u00a050(5), 1873\u20131896 (1989)","journal-title":"International Journal of Control"},{"key":"31_CR12","doi-asserted-by":"crossref","unstructured":"Kruif, B., Vries, T.: Support-Vector-based least squares for learning non-linear dynamics. In: Proceedings of 41st IEEE Conference on Decision and Control, Las Vegas, USA, pp. 10\u201313 (2002)","DOI":"10.1109\/CDC.2002.1184702"},{"key":"31_CR13","doi-asserted-by":"crossref","unstructured":"Gestel, T., Espinoza, M., Suykens, J., Brasseur, C., deMoor, B.: Bayesian input selection for nonlinear regression with LS-SVMS. In: Proceedings of 13th IFAC Symposium on System Identification, Totterdam, The Netherlands, pp. 27\u201329 (2003)","DOI":"10.1016\/S1474-6670(17)34820-6"},{"key":"31_CR14","unstructured":"Valyon, J., Horv\u00e1th, G.: A generalized LS-SVM. In: Principe, J., Gile, L., Morgan, N., Wilson, E. (eds.) Proceedings of 13th IFAC Symposium on System Identification, Rotterdam, The Netherlands (2003)"},{"key":"31_CR15","doi-asserted-by":"publisher","DOI":"10.1142\/5089","volume-title":"Least Square Support Vector Machines","author":"J. Suykens","year":"2002","unstructured":"Suykens, J., van Gestel, T., DeBrabanter, J., DeMoor, B.: Least Square Support Vector Machines. World Scientific, Singapore (2002)"},{"key":"31_CR16","unstructured":"Pontil, M., Mukherjee, S., Girosi, F.: On the noise model of support vector machine regression. A.I. Memo 1651, AI Laboratory, MIT (1998)"},{"key":"31_CR17","series-title":"Lecture Notes in Artificial Intelligence","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1007\/3-540-36187-1_35","volume-title":"AI 2002: Advances in Artificial Intelligence","author":"J. Gao","year":"2002","unstructured":"Gao, J., Gunn, S., Kandola, J.: Adapting kernels by variational approach in SVM. In: McKay, B., Slaney, J.K. (eds.) Canadian AI 2002. LNCS (LNAI), vol.\u00a02557, pp. 395\u2013406. Springer, Heidelberg (2002)"},{"key":"31_CR18","series-title":"Lecture Notes in Artificial Intelligence","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1007\/978-3-540-76928-6_5","volume-title":"AI 2007: Advances in Artificial Intelligence","author":"J. Gao","year":"2007","unstructured":"Gao, J., Xu, R.: Mixture of the robust L1 distributions and its applications. In: Orgun, M.A., Thornton, J. (eds.) AI 2007. LNCS (LNAI), vol.\u00a04830, pp. 26\u201335. Springer, Heidelberg (2007)"},{"key":"31_CR19","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1162\/neco.2007.11-06-397","volume":"20","author":"J. Gao","year":"2008","unstructured":"Gao, J.: Robust L1 principal component analysis and its Bayesian variational inference. Neural Computation\u00a020, 555\u2013572 (2008)","journal-title":"Neural Computation"},{"issue":"2","key":"31_CR20","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1016\/0888-3270(89)90012-5","volume":"3","author":"S. Billings","year":"1989","unstructured":"Billings, S., Chen, S., Backhouse, R.: The identification of linear and nonlinear models of a turbocharged automotive diesel engine. Mech. Syst. Signal Processing\u00a03(2), 123\u2013142 (1989)","journal-title":"Mech. Syst. Signal Processing"}],"container-title":["Lecture Notes in Computer Science","AI 2008: Advances in Artificial Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-540-89378-3_31","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,4]],"date-time":"2025-02-04T17:29:41Z","timestamp":1738690181000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-540-89378-3_31"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2008]]},"ISBN":["9783540893776","9783540893783"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-540-89378-3_31","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2008]]}}}