{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:57:24Z","timestamp":1760245044233,"version":"3.41.0"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319912523"},{"type":"electronic","value":"9783319912530"}],"license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"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":[[2018]]},"DOI":"10.1007\/978-3-319-91253-0_67","type":"book-chapter","created":{"date-parts":[[2018,5,10]],"date-time":"2018-05-10T14:55:13Z","timestamp":1525964113000},"page":"724-735","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Probabilistic Learning Vector Quantization with Cross-Entropy for Probabilistic Class Assignments in Classification Learning"],"prefix":"10.1007","author":[{"given":"Andrea","family":"Villmann","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marika","family":"Kaden","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sascha","family":"Saralajew","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thomas","family":"Villmann","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2018,5,11]]},"reference":[{"key":"67_CR1","series-title":"Santa Fe Institute Studies in the Sciences of Complexity: Lecture Notes","volume-title":"Introduction to the Theory of Neural Computation","author":"JA Hertz","year":"1991","unstructured":"Hertz, J.A., Krogh, A., Palmer, R.G.: Introduction to the Theory of Neural Computation. Santa Fe Institute Studies in the Sciences of Complexity: Lecture Notes, vol. 1. Addison-Wesley, Redwood City (1991)"},{"issue":"5","key":"67_CR2","doi-asserted-by":"publisher","first-page":"845","DOI":"10.1109\/TNNLS.2013.2292894","volume":"25","author":"B Fr\u00e9nay","year":"2014","unstructured":"Fr\u00e9nay, B., Verleysen, M.: Classification in the presence of label noise: a survey. IEEE Trans. Neural Netw. Learn. Syst. 25(5), 845\u2013869 (2014)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"Suppl. 1","key":"67_CR3","first-page":"303","volume":"1","author":"T Kohonen","year":"1988","unstructured":"Kohonen, T.: Learning vector quantization. Neural Netw. 1(Suppl. 1), 303 (1988)","journal-title":"Neural Netw."},{"issue":"2","key":"67_CR4","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1002\/wcs.1378","volume":"7","author":"M Biehl","year":"2016","unstructured":"Biehl, M., Hammer, B., Villmann, T.: Prototype-based models in machine learning. Wiley Interdiscip. Rev.: Cogn. Sci. 7(2), 92\u2013111 (2016)","journal-title":"Wiley Interdiscip. Rev.: Cogn. Sci."},{"key":"67_CR5","unstructured":"Sato, A., Yamada, K.: Generalized learning vector quantization. In: Touretzky, D.S., Mozer, M.C., Hasselmo, M.E. (eds.) Proceedings of the 1995 Conference on Advances in Neural Information Processing Systems, vol. 8, pp. 423\u2013429. MIT Press, Cambridge (1996)"},{"issue":"2","key":"67_CR6","doi-asserted-by":"publisher","first-page":"79","DOI":"10.2478\/fcds-2014-0006","volume":"39","author":"M Kaden","year":"2014","unstructured":"Kaden, M., Lange, M., Nebel, D., Riedel, M., Geweniger, T., Villmann, T.: Aspects in classification learning - review of recent developments in Learning Vector Quantization. Found. Comput. Decis. Sci. 39(2), 79\u2013105 (2014)","journal-title":"Found. Comput. Decis. Sci."},{"issue":"1","key":"67_CR7","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1515\/jaiscr-2017-0005","volume":"7","author":"T Villmann","year":"2017","unstructured":"Villmann, T., Bohnsack, A., Kaden, M.: Can learning vector quantization be an alternative to SVM and deep learning? J. Artif. Intell. Soft Comput. Res. 7(1), 65\u201381 (2017)","journal-title":"J. Artif. Intell. Soft Comput. Res."},{"key":"67_CR8","doi-asserted-by":"publisher","first-page":"1589","DOI":"10.1162\/089976603321891819","volume":"15","author":"S Seo","year":"2003","unstructured":"Seo, S., Obermayer, K.: Soft learning vector quantization. Neural Comput. 15, 1589\u20131604 (2003)","journal-title":"Neural Comput."},{"key":"67_CR9","first-page":"1415","volume":"3","author":"K Torkkola","year":"2003","unstructured":"Torkkola, K.: Feature extraction by non-parametric mutual information maximization. J. Mach. Learn. Res. 3, 1415\u20131438 (2003)","journal-title":"J. Mach. Learn. Res."},{"key":"67_CR10","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436\u2013444 (2015)","journal-title":"Nature"},{"key":"67_CR11","doi-asserted-by":"crossref","unstructured":"Xu, D., Principe, J.: Training MLPs layer-by-layer with the information potential. In: Proceedings of the International Joint Conference on Neural Networks, IJCNN 1999, Los Alamitos, pp. 1045\u20131048. IEEE Press (1999)","DOI":"10.1109\/ICASSP.1999.759922"},{"key":"67_CR12","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4419-1570-2","volume-title":"Information Theoretic Learning","author":"JC Principe","year":"2010","unstructured":"Principe, J.C.: Information Theoretic Learning. Springer, Heidelberg (2010). https:\/\/doi.org\/10.1007\/978-1-4419-1570-2"},{"issue":"1","key":"67_CR13","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1016\/j.neunet.2011.10.001","volume":"26","author":"K Bunte","year":"2012","unstructured":"Bunte, K., Schneider, P., Hammer, B., Schleif, F.-M., Villmann, T., Biehl, M.: Limited rank matrix learning, discriminative dimension reduction and visualization. Neural Netw. 26(1), 159\u2013173 (2012)","journal-title":"Neural Netw."},{"key":"67_CR14","volume-title":"Unsupervised Adaptive Filtering","author":"JC Principe","year":"2000","unstructured":"Principe, J.C., Fischer III, J.W., Xu, D.: Information theoretic learning. In: Haykin, S. (ed.) Unsupervised Adaptive Filtering. Wiley, New York (2000)"},{"issue":"6","key":"67_CR15","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1109\/97.923043","volume":"8","author":"KE Hild","year":"2001","unstructured":"Hild, K.E., Erdogmus, D., Principe, J.: Blind source separation using R\u00e9nyi\u2019s mutual information. IEEE Signal Process. Lett. 8(6), 174\u2013176 (2001)","journal-title":"IEEE Signal Process. Lett."},{"key":"67_CR16","unstructured":"Martinetz, T.: Selbstorganisierende neuronale Netzwerkmodelle zur Bewegungssteuerung. Ph.D.-thesis, Technische Universit\u00e4t M\u00fcnchen, M\u00fcnchen, Germany (1992)"},{"issue":"4","key":"67_CR17","doi-asserted-by":"publisher","first-page":"558","DOI":"10.1109\/72.238311","volume":"4","author":"TM Martinetz","year":"1993","unstructured":"Martinetz, T.M., Berkovich, S.G., Schulten, K.J.: \u2018Neural-gas\u2019 network for vector quantization and its application to time-series prediction. IEEE Trans. Neural Netw. 4(4), 558\u2013569 (1993)","journal-title":"IEEE Trans. Neural Netw."},{"key":"67_CR18","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4612-4016-7","volume-title":"An Information-Theoretic Approach to Neural Computing","author":"G Deco","year":"1997","unstructured":"Deco, G., Obradovic, D.: An Information-Theoretic Approach to Neural Computing. Springer, Heidelberg, New York, Berlin (1997). https:\/\/doi.org\/10.1007\/978-1-4612-4016-7"},{"key":"67_CR19","unstructured":"R\u00e9nyi, A.: On measures of entropy and information. In: Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability, Berkeley. University of California Press (1961)"},{"key":"67_CR20","volume-title":"Probability Theory","author":"A R\u00e9nyi","year":"1970","unstructured":"R\u00e9nyi, A.: Probability Theory. North-Holland Publishing Company, Amsterdam (1970)"},{"key":"67_CR21","volume-title":"Deep Learning","author":"I Goodfellow","year":"2016","unstructured":"Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)"},{"key":"67_CR22","unstructured":"Wittner, B.S., Denker, J.S.: Strategies for teaching layered networks classification tasks. In: Anderson, D.Z. (ed.) Neural Information Processing Systems, pp. 850\u2013859. American Institute of Physics (1988)"},{"issue":"1","key":"67_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1561\/2200000006","volume":"2","author":"Y Bengio","year":"2009","unstructured":"Bengio, Y.: Learning deep architectures for AI. Found. Trends Mach. Learn. 2(1), 1\u2013127 (2009)","journal-title":"Found. Trends Mach. Learn."},{"key":"67_CR24","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"437","DOI":"10.1007\/978-3-642-35289-8_26","volume-title":"Neural Networks: Tricks of the Trade","author":"Y Bengio","year":"2012","unstructured":"Bengio, Y.: Practical recommendations for gradient-based training of deep architectures. In: Montavon, G., Orr, G.B., M\u00fcller, K.-R. (eds.) Neural Networks: Tricks of the Trade. LNCS, vol. 7700, pp. 437\u2013478. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-35289-8_26"}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence and Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-91253-0_67","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,4]],"date-time":"2025-07-04T12:30:37Z","timestamp":1751632237000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-319-91253-0_67"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783319912523","9783319912530"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-91253-0_67","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2018]]},"assertion":[{"value":"11 May 2018","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICAISC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Intelligence and Soft Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Zakopane","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Poland","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 June 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 June 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icaisc2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/icaisc.eu\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}