{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,2]],"date-time":"2025-02-02T20:10:28Z","timestamp":1738527028079,"version":"3.35.0"},"publisher-location":"Berlin, Heidelberg","reference-count":27,"publisher":"Springer Berlin Heidelberg","isbn-type":[{"type":"print","value":"9783540354871"},{"type":"electronic","value":"9783540354888"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"DOI":"10.1007\/978-3-540-35488-8_22","type":"book-chapter","created":{"date-parts":[[2008,11,15]],"date-time":"2008-11-15T10:46:31Z","timestamp":1226745991000},"page":"447-462","source":"Crossref","is-referenced-by-count":2,"title":["Feature Selection via Sensitivity Analysis with Direct Kernel PLS"],"prefix":"10.1007","author":[{"given":"Mark J.","family":"Embrechts","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Robert A.","family":"Bress","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Robert H.","family":"Kewley","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","reference":[{"key":"22_CR1","first-page":"227","volume-title":"Advances in learning theory; methods, models and applications","author":"K.P. Bennett","year":"2003","unstructured":"K.P. Bennett and M.J. Embrechts. An optimization perspective on kernel partial least squares regression. In Advances in learning theory; methods, models and applications, pages 227\u2013250. IOS Press, Amsterdam, 2003."},{"key":"22_CR2","unstructured":"J. Bi and K.P. Bennett. Regression error characteristic curves. In Proceedings of the 20th International Conference on Machine Learning (ICML 2003), Washington DC, 2003."},{"key":"22_CR3","doi-asserted-by":"crossref","unstructured":"B.E. Boser, I. Guyon, and V. Vapnik. A training algorithm for optimal margin classifiers. In Fifth Annual Workshop on Computational Learning Theory, pages 144\u2013152. ACM, 1992.","DOI":"10.1145\/130385.130401"},{"issue":"2","key":"22_CR4","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1023\/A:1018012322525","volume":"22","author":"L. Breiman","year":"1996","unstructured":"L. Breiman. Bagging predictors. Machine Learning, 22(2):123\u2013140, 1996.","journal-title":"Machine Learning"},{"key":"22_CR5","doi-asserted-by":"crossref","unstructured":"N. Cristianini and J. Shawe-Taylor. An Introduction to Support Vector Machines and Other Kernel-Based Methods. Cambridge University Press, 2000.","DOI":"10.1017\/CBO9780511801389"},{"key":"22_CR6","unstructured":"M.J. Embrechts and K.P. Bennett. Sparse-kernel pls for molecular drug design. In Y. Bengio, editor, Proceedings of the Learning Workshop, Snowbird, Utah, 2002."},{"issue":"5","key":"22_CR7","doi-asserted-by":"publisher","first-page":"385","DOI":"10.1053\/pcad.2002.122693","volume":"44","author":"V. Froelicher","year":"2002","unstructured":"V. Froelicher, K. Shetler, and E. Ashley. Better decisions through science: exercise testing scores. Progress in Cardiovascular Diseases, 44(5):385\u2013414, 2002.","journal-title":"Progress in Cardiovascular Diseases"},{"issue":"6","key":"22_CR8","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1155\/S1463924698000224","volume":"20","author":"L. Gao","year":"1998","unstructured":"L. Gao and S. Ren. Simultaneous spectrometric determination of manganese, zinc and cobalt by kernel partial least-squares method. Journal of Automatic Chemistry, 20(6):179\u2013183, 1998.","journal-title":"Journal of Automatic Chemistry"},{"key":"22_CR9","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1016\/S0169-7439(98)00091-4","volume":"45","author":"L. Gao","year":"1999","unstructured":"L. Gao and S. Ren. Simultaneous spectrophotometric determination of four metals by the kernel partial least squares method. Chemometrics and Intelligent Laboratory Systems, 45:87\u201393, 1999.","journal-title":"Chemometrics and Intelligent Laboratory Systems"},{"key":"22_CR10","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1016\/S1093-3263(01)00123-1","volume":"20","author":"A. Golbraikh","year":"2002","unstructured":"A. Golbraikh and A. Tropsha. Beware of q2! Journal of Molecular Graphics and Modelling, 20:269\u2013276, 2002.","journal-title":"Journal of Molecular Graphics and Modelling"},{"key":"22_CR11","doi-asserted-by":"publisher","first-page":"69","DOI":"10.2307\/1267352","volume":"12","author":"A.E. Hoerl","year":"1970","unstructured":"A.E. Hoerl and R.W. Kennard. Ridge regression: biased estimation for nonorthogonal problems. Technometrics, 12:69\u201382, 1970.","journal-title":"Technometrics"},{"key":"22_CR12","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1002\/(SICI)1097-0266(199902)20:2<195::AID-SMJ13>3.0.CO;2-7","volume":"20","author":"J. Hulland","year":"1999","unstructured":"J. Hulland. Use of partial least squares (PLS) in strategic management research: a review of four recent studies. Strategic Management Journal, 20:195\u2013204, 1999.","journal-title":"Strategic Management Journal"},{"key":"22_CR13","doi-asserted-by":"publisher","first-page":"889","DOI":"10.2307\/2589281","volume":"105","author":"I.C.F. Ipsen","year":"1998","unstructured":"I.C.F. Ipsen and C.D. Meyer. The idea behind krylov methods. American Mathematical Monthly, 105:889\u2013899, 1998.","journal-title":"American Mathematical Monthly"},{"key":"22_CR14","unstructured":"R.A. Johnson and D.A. Wichern. Applied multivariate statistical analysis (second edition). Prentice Hall, 2000."},{"key":"22_CR15","doi-asserted-by":"publisher","first-page":"487","DOI":"10.1162\/089976603762553013","volume":"15","author":"S.S. Keerthi","year":"2003","unstructured":"S.S. Keerthi and S.K. Shevade. SMO algorithm for least squares SVM formulations. Neural Computation, 15:487\u2013507, 2003.","journal-title":"Neural Computation"},{"issue":"3","key":"22_CR16","doi-asserted-by":"publisher","first-page":"668","DOI":"10.1109\/72.846738","volume":"11","author":"R.H. Kewley","year":"2000","unstructured":"R.H. Kewley and M.J. Embrechts. Data strip mining for the virtual design of pharmaceuticals with neural networks. IEEE Transactions on Neural Networks, 11(3):668\u2013679, 2000.","journal-title":"IEEE Transactions on Neural Networks"},{"key":"22_CR17","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1002\/cem.1180070104","volume":"7","author":"F. Lindgren","year":"1993","unstructured":"F. Lindgren, P. Geladi, and S. Wold. The kernel algorithm for pls. Journal of Chemometrics, 7:45\u201359, 1993.","journal-title":"Journal of Chemometrics"},{"issue":"3","key":"22_CR18","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1155\/S1463924695000204","volume":"17","author":"S. Ren","year":"1995","unstructured":"S. Ren and L. Gao. Simultaneous spectrophotometric determination of copper (II), lead (II) and cadmium (II). Journal of Automatic Chemistry, 17(3):115\u2013118, 1995.","journal-title":"Journal of Automatic Chemistry"},{"key":"22_CR19","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1162\/15324430260185556","volume":"2","author":"R. Rosipal","year":"2001","unstructured":"R. Rosipal and L. T. Trejo. Kernel partial least squares regression in reproducing kernel hilbert space. Journal of Machine Learning Research, 2:97\u2013123, 2001.","journal-title":"Journal of Machine Learning Research"},{"key":"22_CR20","doi-asserted-by":"crossref","unstructured":"B. Sch\u00f6lkopf and A. Smola. Learning with kernels: Support Vector Machines, regularization, optimization and beyond. MIT Press, 2001.","DOI":"10.7551\/mitpress\/4175.001.0001"},{"key":"22_CR21","volume-title":"Least squares support vector machines","author":"J.A.K. Suykens","year":"2003","unstructured":"J.A.K. Suykens, T. Van Gestel, J. de Brabanter, B. De Moor, and J. Vandewalle. Least squares support vector machines. World Scientific Publishing Company, Singapore, 2003."},{"issue":"3","key":"22_CR22","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1023\/A:1018628609742","volume":"9","author":"J.A.K. Suykens","year":"1999","unstructured":"J.A.K. Suykens and J. Vandewalle. Least-squares support vector machine classifiers. Neural Processing Letters, 9(3):293\u2013300, 1999.","journal-title":"Neural Processing Letters"},{"key":"22_CR23","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1038\/scientificamerican1000-82","volume":"283","author":"J.A. Swets","year":"2000","unstructured":"J.A. Swets, R.M. Dawes, and J. Monahan. Better decisions through science. Scientific American, 283:82\u201387, 2000.","journal-title":"Scientific American"},{"key":"22_CR24","first-page":"391","volume-title":"Multivariate analysis","author":"H. Wold","year":"1966","unstructured":"H. Wold. Estimation of principal components and related models by iterative least squares. In P.R. Krishnaiah, editor, Multivariate analysis, pages 391\u2013420. Academic Press, New York, 1966."},{"key":"22_CR25","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1016\/B978-0-12-103950-9.50017-4","volume-title":"Quantitative sociology: International perspectives on mathematical and statistical model building","author":"H. Wold","year":"1975","unstructured":"H. Wold. Path models with latent variables: the NIPALS approach. In H.M. Balock, editor, Quantitative sociology: International perspectives on mathematical and statistical model building, pages 307\u2013357. Academic Press, New York, 1975."},{"key":"22_CR26","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1016\/S0169-7439(01)00152-6","volume":"58","author":"S. Wold","year":"2001","unstructured":"S. Wold. Personal memories of the early PLS development. Chemometrics and Intelligent Laboratory Systems, 58:83\u201384, 2001.","journal-title":"Chemometrics and Intelligent Laboratory Systems"},{"key":"22_CR27","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1016\/S0169-7439(01)00155-1","volume":"58","author":"S. Wold","year":"2001","unstructured":"S. Wold, M. Sj\u00f6lstr\u00f6m, and M.L. Erikson. PLS-regression: a basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems, 58:109\u2013130, 2001.","journal-title":"Chemometrics and Intelligent Laboratory Systems"}],"container-title":["Studies in Fuzziness and Soft Computing","Feature Extraction"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-540-35488-8_22.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,2]],"date-time":"2025-02-02T19:56:01Z","timestamp":1738526161000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-540-35488-8_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[null]]},"ISBN":["9783540354871","9783540354888"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-540-35488-8_22","relation":{},"ISSN":["1434-9922"],"issn-type":[{"type":"print","value":"1434-9922"}],"subject":[]}}