{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:43:35Z","timestamp":1742913815875,"version":"3.40.3"},"publisher-location":"Cham","reference-count":41,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319235240"},{"type":"electronic","value":"9783319235257"}],"license":[{"start":{"date-parts":[[2015,1,1]],"date-time":"2015-01-01T00:00:00Z","timestamp":1420070400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2015,1,1]],"date-time":"2015-01-01T00:00:00Z","timestamp":1420070400000},"content-version":"vor","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":[[2015]]},"DOI":"10.1007\/978-3-319-23525-7_9","type":"book-chapter","created":{"date-parts":[[2015,8,28]],"date-time":"2015-08-28T08:20:13Z","timestamp":1440750013000},"page":"137-153","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Opening the Black Box: Revealing Interpretable Sequence Motifs in Kernel-Based Learning Algorithms"],"prefix":"10.1007","author":[{"given":"Marina M.-C.","family":"Vidovic","sequence":"first","affiliation":[]},{"given":"Nico","family":"G\u00f6rnitz","sequence":"additional","affiliation":[]},{"given":"Klaus-Robert","family":"M\u00fcller","sequence":"additional","affiliation":[]},{"given":"Gunnar","family":"R\u00e4tsch","sequence":"additional","affiliation":[]},{"given":"Marius","family":"Kloft","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2015,8,29]]},"reference":[{"key":"9_CR1","doi-asserted-by":"crossref","unstructured":"Abeel, T., de Peer, Y.V., Saeys, Y.: Towards a gold standard for promoter prediction evaluation. Bioinformatics (2009)","DOI":"10.1093\/bioinformatics\/btp191"},{"key":"9_CR2","doi-asserted-by":"crossref","unstructured":"Bach, S., Binder, A., Montavon, G., Klauschen, F., M\u00fcller, K.R., Samek, W.: On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PLOS ONE (2015)","DOI":"10.1371\/journal.pone.0130140"},{"key":"9_CR3","first-page":"1803","volume":"11","author":"D Baehrens","year":"2010","unstructured":"Baehrens, D., Schroeter, T., Harmeling, S., Kawanabe, M., Hansen, K., M\u00fcller, K.R.: How to explain individual classification decisions. JMLR 11, 1803\u20131831 (2010)","journal-title":"JMLR"},{"issue":"10","key":"9_CR4","doi-asserted-by":"publisher","first-page":"e1000173","DOI":"10.1371\/journal.pcbi.1000173","volume":"4","author":"A Ben-Hur","year":"2008","unstructured":"Ben-Hur, A., Ong, C.S., Sonnenburg, S., Sch\u00f6lkopf, B., R\u00e4tsch, G.: Support vector machines and kernels for computational biology. PLoS Comput Biology 4(10), e1000173 (2008). http:\/\/www.ploscompbiol.org\/article\/info:doi\/10.1371\/journal.pcbi.1000173","journal-title":"PLoS Comput Biology"},{"key":"9_CR5","doi-asserted-by":"crossref","unstructured":"Boser, B., Guyon, I., Vapnik, V.: A training algorithm for optimal margin classifiers. In: Haussler, D. (ed.) COLT. pp. 144\u2013152. ACM (1992)","DOI":"10.1145\/130385.130401"},{"issue":"10","key":"9_CR6","doi-asserted-by":"publisher","first-page":"2130","DOI":"10.1016\/j.ins.2006.12.003","volume":"177","author":"KL Chung","year":"2007","unstructured":"Chung, K.L., Huang, Y.L., Liu, Y.W.: Efficient algorithms for coding hilbert curve of arbitrary-sized image and application to window query. Information Sciences 177(10), 2130\u20132151 (2007)","journal-title":"Information Sciences"},{"key":"9_CR7","first-page":"273","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes, C., Vapnik, V.: Support vector networks. Machine Learning 20, 273\u2013297 (1995)","journal-title":"Machine Learning"},{"key":"9_CR8","doi-asserted-by":"publisher","first-page":"1188","DOI":"10.1101\/gr.849004","volume":"14","author":"G Crooks","year":"2004","unstructured":"Crooks, G., Hon, G., Chandonia, J., Brenner, S.: Weblogo: A sequence logo generator. Genome Research 14, 1188\u20131190 (2004)","journal-title":"Genome Research"},{"key":"9_CR9","doi-asserted-by":"crossref","unstructured":"Dafner, R., Cohen-Or, D., Matias, Y.: Context-based space filling curves. In: Computer Graphics Forum, vol. 19, pp. 209\u2013218. Wiley Online Library (2000)","DOI":"10.1111\/1467-8659.00413"},{"key":"9_CR10","unstructured":"Goernitz, N., Braun, M., Kloft, M.: Hidden markov anomaly detection. In: Proceedings of The 32nd International Conference on Machine Learning, pp. 1833\u20131842 (2015)"},{"key":"9_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1007\/978-3-642-04180-8_44","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"N G\u00f6rnitz","year":"2009","unstructured":"G\u00f6rnitz, N., Kloft, M., Brefeld, U.: Active and semi-supervised data domain description. In: Buntine, W., Grobelnik, M., Mladeni\u0107, D., Shawe-Taylor, J. (eds.) ECML PKDD 2009, Part I. LNCS, vol. 5781, pp. 407\u2013422. Springer, Heidelberg (2009)"},{"key":"9_CR12","doi-asserted-by":"crossref","unstructured":"G\u00f6rnitz, N., Kloft, M., Rieck, K., Brefeld, U.: Active learning for network intrusion detection. In: AISEC, p. 47. ACM Press (2009)","DOI":"10.1145\/1654988.1655002"},{"key":"9_CR13","doi-asserted-by":"crossref","unstructured":"G\u00f6rnitz, N., Kloft, M.M., Rieck, K., Brefeld, U.: Toward supervised anomaly detection. Journal of Artificial Intelligence Research (2013)","DOI":"10.1613\/jair.3623"},{"key":"9_CR14","doi-asserted-by":"crossref","unstructured":"Hansen, K., Baehrens, D., Schroeter, T., Rupp, M., M\u00fcller, K.R.: Visual interpretation of kernel-based prediction models. Molecular Informatics 30(9), September 2011. WILEY-VCH Verlag","DOI":"10.1002\/minf.201100059"},{"key":"9_CR15","doi-asserted-by":"crossref","unstructured":"Hastie, T., Tibshirani, R., Friedman, J., Hastie, T., Friedman, J., Tibshirani, R.: The elements of statistical learning, vol. 2. Springer (2009)","DOI":"10.1007\/978-0-387-84858-7"},{"issue":"5","key":"9_CR16","doi-asserted-by":"publisher","first-page":"550","DOI":"10.1109\/34.291440","volume":"16","author":"JJ Hull","year":"1994","unstructured":"Hull, J.J.: A database for handwritten text recognition research. IEEE Transactions on Pattern Analysis and Machine Intelligence 16(5), 550\u2013554 (1994)","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"9_CR17","first-page":"953","volume":"12","author":"M Kloft","year":"2011","unstructured":"Kloft, M., Brefeld, U., Sonnenburg, S., Zien, A.: lp-Norm Multiple Kernel Learning. JMLR 12, 953\u2013997 (2011)","journal-title":"JMLR"},{"key":"9_CR18","doi-asserted-by":"crossref","unstructured":"Kloft, M., Brefeld, U., D\u00fcessel, P., Gehl, C., Laskov, P.: Automatic feature selection for anomaly detection. In: Proceedings of the 1st ACM Workshop on AISec, pp. 71\u201376. ACM (2008)","DOI":"10.1145\/1456377.1456395"},{"issue":"22","key":"9_CR19","first-page":"997","volume":"22","author":"M Kloft","year":"2009","unstructured":"Kloft, M., Brefeld, U., Sonnenburg, S., Laskov, P., M\u00fcller, K.R., Zien, A.: Efficient and accurate lp-norm multiple kernel learning. Advances in Neural Information Processing Systems 22(22), 997\u20131005 (2009)","journal-title":"Advances in Neural Information Processing Systems"},{"key":"9_CR20","unstructured":"Kloft, M., Laskov, P.: Online anomaly detection under adversarial impact. In: AISTATS, pp. 405\u2013412 (2010)"},{"key":"9_CR21","doi-asserted-by":"crossref","unstructured":"Kloft, M., R\u00fcckert, U., Bartlett, P.: A unifying view of multiple kernel learning. Machine Learning and Knowledge Discovery in Databases pp. 66\u201381 (2010)","DOI":"10.1007\/978-3-642-15883-4_5"},{"key":"9_CR22","unstructured":"Leslie, C.S., Eskin, E., Noble, W.S.: The spectrum kernel: A string kernel for svm protein classification. In: Pacific Symposium on Biocomputing, pp. 566\u2013575 (2002)"},{"issue":"3","key":"9_CR23","doi-asserted-by":"publisher","first-page":"503","DOI":"10.1007\/BF01589116","volume":"45","author":"DC Liu","year":"1989","unstructured":"Liu, D.C., Nocedal, J.: On the limited memory BFGS method for large scale optimization. Math. Program. 45(3), 503\u2013528 (1989). http:\/\/dx.doi.org\/10.1007\/BF01589116","journal-title":"Math. Program."},{"key":"9_CR24","unstructured":"Mohri, M., Rostamizadeh, A., Talwalkar, A.: Foundations of machine learning. MIT press (2012)"},{"issue":"4","key":"9_CR25","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1109\/MSP.2013.2249294","volume":"30","author":"G Montavon","year":"2013","unstructured":"Montavon, G., Braun, M.L., Krueger, T., M\u00fcller, K.R.: Analyzing local structure in kernel-based learning: Explanation, complexity and reliability assessment. Signal Processing Magazine, IEEE 30(4), 62\u201374 (2013)","journal-title":"Signal Processing Magazine, IEEE"},{"issue":"2","key":"9_CR26","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1109\/72.914517","volume":"12","author":"KR M\u00fcller","year":"2001","unstructured":"M\u00fcller, K.R., Mika, S., R\u00e4tsch, G., Tsuda, K., Sch\u00f6lkopf, B.: An introduction to kernel-based learning algorithms. IEEE Transactions on Neural Networks 12(2), 181\u2013201 (2001). http:\/\/dx.doi.org\/10.1109\/72.914517","journal-title":"IEEE Transactions on Neural Networks"},{"issue":"2","key":"9_CR27","doi-asserted-by":"publisher","first-page":"e20","DOI":"10.1371\/journal.pcbi.0030020","volume":"3","author":"G R\u00e4tsch","year":"2007","unstructured":"R\u00e4tsch, G., Sonnenburg, S., Srinivasan, J., Witte, H., M\u00fcller, K.R., Sommer, R.J., Sch\u00f6lkopf, B.: Improving the caenorhabditis elegans genome annotation using machine learning. PLoS Comput. Biol. 3(2), e20 (2007)","journal-title":"PLoS Comput. Biol."},{"key":"9_CR28","doi-asserted-by":"crossref","unstructured":"R\u00e4tsch, G., Sonnenburg, S.: Accurate splice site prediction for caenorhabditis elegans. Kernel Methods in Computational Biology, 277\u2013298 (2004). MIT Press series on Computational Molecular Biology, MIT Press","DOI":"10.7551\/mitpress\/4057.003.0018"},{"issue":"Database\u2013Issue","key":"9_CR29","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1093\/nar\/gkh012","volume":"32","author":"A Sandelin","year":"2004","unstructured":"Sandelin, A., Alkema, W., Engstr\u00f6m, P., Wasserman, W.W., Lenhard, B.: Jaspar: an open-access database for eukaryotic transcription factor binding profiles. Nucleic Acids Research 32(Database\u2013Issue), 91\u201394 (2004)","journal-title":"Nucleic Acids Research"},{"issue":"3","key":"9_CR30","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1007\/s10142-003-0086-6","volume":"3","author":"A Sandelin","year":"2003","unstructured":"Sandelin, A., H\u00f6glund, A., Lenhardd, B., Wasserman, W.W.: Integrated analysis of yeast regulatory sequences for biologically linked clusters of genes. Functional & Integrative Genomics 3(3), 125\u2013134 (2003)","journal-title":"Functional & Integrative Genomics"},{"key":"9_CR31","volume-title":"Learning with Kernels","author":"B Sch\u00f6lkopf","year":"2002","unstructured":"Sch\u00f6lkopf, B., Smola, A.: Learning with Kernels. MIT Press, Cambridge (2002)"},{"issue":"5","key":"9_CR32","doi-asserted-by":"publisher","first-page":"1299","DOI":"10.1162\/089976698300017467","volume":"10","author":"B Sch\u00f6lkopf","year":"1998","unstructured":"Sch\u00f6lkopf, B., Smola, A., M\u00fcller, K.R.: Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation 10(5), 1299\u20131319 (1998)","journal-title":"Neural Computation"},{"key":"9_CR33","first-page":"1799","volume":"11","author":"S Sonnenburg","year":"2010","unstructured":"Sonnenburg, S., R\u00e4tsch, G., Henschel, S., Widmer, C., Behr, J., Zien, A., Bona, F.D., Binder, A., Gehl, C., Franc, V.: The SHOGUN machine learning toolbox. Journal of Machine Learning Research 11, 1799\u20131802 (2010)","journal-title":"Journal of Machine Learning Research"},{"key":"9_CR34","first-page":"1531","volume":"7","author":"S Sonnenburg","year":"2006","unstructured":"Sonnenburg, S., R\u00e4tsch, G., Sch\u00e4fer, C., Sch\u00f6lkopf, B.: Large scale multiple kernel learning. Journal of Machine Learning Research 7, 1531\u20131565 (2006)","journal-title":"Journal of Machine Learning Research"},{"key":"9_CR35","doi-asserted-by":"crossref","unstructured":"Sonnenburg, S., Zien, A., Philips, P., R\u00e4tsch, G.: POIMs: positional oligomer importance matrices \u2013 understanding support vector machine based signal detectors. Bioinformatics (2008). (received the Outstanding Student Paper Award at ISMB 2008)","DOI":"10.1093\/bioinformatics\/btn170"},{"key":"9_CR36","unstructured":"Sonnenburg, S., Franc, V.: Coffin: a computational framework for linear SVMs. In: ICML, pp. 999\u20131006 (2010)"},{"key":"9_CR37","doi-asserted-by":"crossref","unstructured":"Sonnenburg, S., Schweikert, G., Philips, P., Behr, J., R\u00e4tsch, G.: Accurate Splice Site Prediction. BMC Bioinformatics, Special Issue from NIPS workshop on New Problems and Methods in Computational Biology Whistler, Canada, December 18, 2006, vol. 8(Suppl. 10), p. S7, December 2007","DOI":"10.1186\/1471-2105-8-S10-S7"},{"issue":"14","key":"9_CR38","doi-asserted-by":"publisher","first-page":"e472","DOI":"10.1093\/bioinformatics\/btl250","volume":"22","author":"S Sonnenburg","year":"2006","unstructured":"Sonnenburg, S., Zien, A., R\u00e4tsch, G.: ARTS: Accurate Recognition of Transcription Starts in Human. Bioinformatics 22(14), e472\u2013480 (2006)","journal-title":"Bioinformatics"},{"key":"9_CR39","unstructured":"Zeller, G., Goernitz, N., Kahles, A., Behr, J., Mudrakarta, P., Sonnenburg, S., Raetsch, G.: mtim: rapid and accurate transcript reconstruction from rna-seq data. arXiv preprint arXiv:1309.5211 (2013)"},{"key":"9_CR40","unstructured":"Zien, A., Philips, P., Sonnenburg, S.: Computing Positional Oligomer Importance Matrices (POIMs). Research Report; Electronic Publication 2, Fraunhofer Institute FIRST, December 2007"},{"issue":"9","key":"9_CR41","doi-asserted-by":"publisher","first-page":"799","DOI":"10.1093\/bioinformatics\/16.9.799","volume":"16","author":"A Zien","year":"2000","unstructured":"Zien, A., R\u00e4tsch, G., Mika, S., Sch\u00f6lkopf, B., Lengauer, T., M\u00fcller, K.R.: Engineering support vector machine kernels that recognize translation initiation sites in DNA. BioInformatics 16(9), 799\u2013807 (2000)","journal-title":"BioInformatics"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-23525-7_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,10]],"date-time":"2024-06-10T20:00:40Z","timestamp":1718049640000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-319-23525-7_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015]]},"ISBN":["9783319235240","9783319235257"],"references-count":41,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-23525-7_9","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2015]]},"assertion":[{"value":"29 August 2015","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}