{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T09:27:29Z","timestamp":1743067649735,"version":"3.40.3"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030196417"},{"type":"electronic","value":"9783030196424"}],"license":[{"start":{"date-parts":[[2019,4,28]],"date-time":"2019-04-28T00:00:00Z","timestamp":1556409600000},"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":[[2020]]},"DOI":"10.1007\/978-3-030-19642-4_18","type":"book-chapter","created":{"date-parts":[[2019,4,27]],"date-time":"2019-04-27T10:04:21Z","timestamp":1556359461000},"page":"179-188","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Investigation of Activation Functions for Generalized Learning Vector Quantization"],"prefix":"10.1007","author":[{"given":"Thomas","family":"Villmann","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jensun","family":"Ravichandran","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrea","family":"Villmann","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David","family":"Nebel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marika","family":"Kaden","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,4,28]]},"reference":[{"issue":"Suppl 1","key":"18_CR1","first-page":"303","volume":"1","author":"T Kohonen","year":"1988","unstructured":"Kohonen T (1988) Learning vector quantization. Neural Netw 1(Suppl 1):303","journal-title":"Neural Netw"},{"key":"18_CR2","unstructured":"Villmann T, Saralajew S, Villmann A, Kaden M (2018) Learning vector quantization methods for interpretable classification learning and multilayer networks. In: Sabourin C, Merelo JJ, Barranco AL, Madani K, Warwick K (eds) Proceedings of the 10th international joint conference on computational intelligence (IJCCI), Sevilla. SCITEPRESS - Science and Technology Publications, Lda., Lisbon, pp 15\u201321. ISBN 978-989-758-327-8"},{"key":"18_CR3","unstructured":"Sato A, Yamada K (1996) Generalized learning vector quantization. In: Touretzky DS, Mozer MC, Hasselmo ME (eds) Advances in neural information processing systems 8, Proceedings of the 1995 conference. MIT Press, Cambridge, pp 423\u2013429"},{"key":"18_CR4","unstructured":"Crammer K, Gilad-Bachrach R, Navot A, Tishby A (2003) Margin analysis of the LVQ algorithm. In: Becker S, Thrun S, Obermayer K (eds) Advances in neural information processing (Proceedings of NIPS 2002), vol 15. MIT Press, Cambridge, pp 462\u2013469"},{"key":"18_CR5","doi-asserted-by":"publisher","first-page":"3532","DOI":"10.1162\/neco.2009.11-08-908","volume":"21","author":"P Schneider","year":"2009","unstructured":"Schneider P, Hammer B, Biehl M (2009) Adaptive relevance matrices in learning vector quantization. Neural Comput 21:3532\u20133561","journal-title":"Neural Comput"},{"key":"#cr-split#-18_CR6.1","unstructured":"de Vries H, Memisevic R, Courville A (2016) Deep learning vector quantization. In: Verleysen M"},{"key":"#cr-split#-18_CR6.2","unstructured":"(ed) Proceedings of the European symposium on artificial neural networks, computational intelligence and machine learning (ESANN 2016), Louvain-La-Neuve, Belgium, pp 503-508. i6doc.com"},{"key":"18_CR7","doi-asserted-by":"crossref","unstructured":"Villmann T, Biehl M, Villmann A, Saralajew S (2017) Fusion of deep learning architectures, multilayer feedforward networks and learning vector quantizers for deep classification learning. In: Proceedings of the 12th workshop on self-organizing maps and learning vector quantization (WSOM2017+). IEEE Press, pp 248\u2013255","DOI":"10.1109\/WSOM.2017.8020009"},{"key":"18_CR8","series-title":"Springer series in information sciences","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-642-97610-0","volume-title":"Self-organizing maps","author":"T Kohonen","year":"1995","unstructured":"Kohonen T (1995) Self-organizing maps, vol 30. Springer series in information sciences. Springer, Heidelberg (Second Extended Edition 1997)"},{"key":"18_CR9","volume-title":"Neural networks. A comprehensive foundation","author":"S Haykin","year":"1994","unstructured":"Haykin S (1994) Neural networks. A comprehensive foundation. Macmillan, New York"},{"key":"18_CR10","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 JA, Krogh A, Palmer RG (1991) Introduction to the theory of neural computation, vol 1. Santa Fe institute studies in the sciences of complexity: lecture notes. Addison-Wesley, Redwood City"},{"key":"18_CR11","volume-title":"Deep learning","author":"I Goodfellow","year":"2016","unstructured":"Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT Press, Cambridge"},{"key":"18_CR12","unstructured":"Ramachandran P, Zoph B, Le QV (2018) Swish: a self-gated activation function. Technical report arXiv:1710.05941v2 , Google brain"},{"key":"18_CR13","unstructured":"Ramachandran P, Zoph B, Le QV (2018) Searching for activation functions. Technical report arXiv:1710.05941v1 , Google brain"},{"key":"18_CR14","doi-asserted-by":"crossref","unstructured":"Eger S, Youssef P, Gurevych I (2018) Is it time to swish? comparing deep learning activation functions across NLP tasks. In: Proceedings of the 2018 conference on empirical methods in natural language processing (EMNLP), Brussels, Belgium. Association for computational linguistics, pp 4415\u20134424","DOI":"10.18653\/v1\/D18-1472"},{"issue":"2","key":"18_CR15","doi-asserted-by":"publisher","first-page":"76","DOI":"10.26555\/ijain.v4i2.249","volume":"4","author":"HH Chieng","year":"2018","unstructured":"Chieng HH, Wahid N, Pauline O, Perla SRK (2018) Flatten-T swish: a thresholded ReLU-swish-like activation function for deep learning. Int J Adv Intell Inform 4(2):76\u201386","journal-title":"Int J Adv Intell Inform"},{"issue":"9","key":"18_CR16","doi-asserted-by":"publisher","first-page":"2423","DOI":"10.1007\/s00500-014-1496-1","volume":"19","author":"M Kaden","year":"2015","unstructured":"Kaden M, Riedel M, Hermann W, Villmann T (2015) Border-sensitive learning in generalized learning vector quantization: an alternative to support vector machines. Soft Comput 19(9):2423\u20132434","journal-title":"Soft Comput"},{"key":"18_CR17","doi-asserted-by":"crossref","unstructured":"Fletcher R (1987) Practical methods of optimization, 2nd edn. Wiley, New York. 2000 edition","DOI":"10.1002\/9781118723203"},{"key":"18_CR18","doi-asserted-by":"crossref","unstructured":"LeKander M, Biehl M, de Vries H (2017) Empirical evaluation of gradient methods for matrix learning vector quantization. In: Proceedings of the 12th workshop on self-organizing maps and learning vector quantization (WSOM2017+). IEEE Press, pp 1\u20138","DOI":"10.1109\/WSOM.2017.8020027"},{"issue":"8\u20139","key":"18_CR19","doi-asserted-by":"publisher","first-page":"1059","DOI":"10.1016\/S0893-6080(02)00079-5","volume":"15","author":"B Hammer","year":"2002","unstructured":"Hammer B, Villmann T (2002) Generalized relevance learning vector quantization. Neural Netw 15(8\u20139):1059\u20131068","journal-title":"Neural Netw"},{"key":"18_CR20","unstructured":"Saralajew S, Holdijk L, Rees M, Kaden M, Villmann T (2018) Prototype-based neural network layers: incorporating vector quantization. Mach Learn Rep 12(MLR-03-2018):1\u201317. ISSN: 1865-3960, http:\/\/www.techfak.uni-bielefeld.de\/~fschleif\/mlr\/mlr_03_2018.pdf"},{"key":"18_CR21","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.neunet.2017.12.012","volume":"107","author":"S Elfwing","year":"2018","unstructured":"Elfwing S, Uchibe E, Doya K (2018) Sigmoid-weighted linear units for neural network function approximation in reinforcement learning. Neural Netw 107:3\u201311","journal-title":"Neural Netw"},{"key":"18_CR22","first-page":"4944","volume-title":"Advances in neural information processing systems 31","author":"H Zhang","year":"2018","unstructured":"Zhang H, Weng T-W, Chen P-Y, Hsieh C-J, Daniel L (2018) Efficient neural network robustness certification with general activation functions. In: Bengio S, Wallach H, Larochelle H, Grauman K, Cesa-Bianchi N, Garnett R (eds) Advances in neural information processing systems 31. Curran Associates, Inc., New York, pp 4944\u20134953"},{"key":"18_CR23","unstructured":"Cook J (2011) Basic properties of the soft maximum. Working paper series 70, UT MD Anderson cancer center department of biostatistics. http:\/\/biostats.bepress.com\/mdandersonbiostat\/paper70"},{"key":"18_CR24","unstructured":"Lange M, Villmann T (2013) Derivatives of $$l_p$$ -norms and their approximations. Mach. Learn. Rep. 7(MLR-04-2013):43\u201359. ISSN: 1865-3960. http:\/\/www.techfak.uni-bielefeld.de\/~fschleif\/mlr\/mlr_04_2013.pdf"},{"key":"18_CR25","unstructured":"Maas AL, Hannun AY, Ng AY (2013) Rectifier nonlinearities improve neural network acoustic models. In: Proceedings of ICML-workshop for on deep learning for audio, speech, and language processing, Proceedings of machine learning research, vol 28"},{"issue":"1","key":"18_CR26","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/j.chemolab.2007.09.004","volume":"91","author":"C Krier","year":"2008","unstructured":"Krier C, Rossi F, Fran\u00e7ois D, Verleysen M (2008) A data-driven functional projection approach for the selection of feature ranges in spectra with ICA or cluster analysis. Chemometr Intell Lab Syst 91(1):43\u201353","journal-title":"Chemometr Intell Lab Syst"},{"key":"18_CR27","doi-asserted-by":"publisher","DOI":"10.1002\/0471723800","volume-title":"Signal theory methods in multispectral remote sensing","author":"DA Landgrebe","year":"2003","unstructured":"Landgrebe DA (2003) Signal theory methods in multispectral remote sensing. Wiley, Hoboken"},{"key":"18_CR28","unstructured":"Asuncion A, Newman DJ: UC Irvine machine learning repository. http:\/\/archive.ics.uci.edu\/ml\/"}],"container-title":["Advances in Intelligent Systems and Computing","Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-19642-4_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,11,24]],"date-time":"2019-11-24T03:26:50Z","timestamp":1574566010000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-19642-4_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,4,28]]},"ISBN":["9783030196417","9783030196424"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-19642-4_18","relation":{},"ISSN":["2194-5357","2194-5365"],"issn-type":[{"type":"print","value":"2194-5357"},{"type":"electronic","value":"2194-5365"}],"subject":[],"published":{"date-parts":[[2019,4,28]]},"assertion":[{"value":"28 April 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"WSOM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Self-Organizing Maps","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Barcelona","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 June 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 June 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"wsom2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/samm.univ-paris1.fr\/WSOM-2017","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}