{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:57:26Z","timestamp":1760245046351,"version":"3.41.0"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319491295"},{"type":"electronic","value":"9783319491301"}],"license":[{"start":{"date-parts":[[2016,1,1]],"date-time":"2016-01-01T00:00:00Z","timestamp":1451606400000},"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":[[2016]]},"DOI":"10.1007\/978-3-319-49130-1_6","type":"book-chapter","created":{"date-parts":[[2016,11,4]],"date-time":"2016-11-04T14:13:59Z","timestamp":1478268839000},"page":"65-75","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Spam Filtering Using Regularized Neural Networks with Rectified Linear Units"],"prefix":"10.1007","author":[{"given":"Aliaksandr","family":"Barushka","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Petr","family":"H\u00e1jek","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2016,11,5]]},"reference":[{"issue":"4","key":"6_CR1","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1561\/1500000006","volume":"1","author":"GV Cormack","year":"2006","unstructured":"Cormack, G.V.: Email spam filtering: a systematic review. Found. Trends Inf. Retrieval 1(4), 335\u2013455 (2006)","journal-title":"Found. Trends Inf. Retrieval"},{"issue":"10","key":"6_CR2","doi-asserted-by":"publisher","first-page":"9899","DOI":"10.1016\/j.eswa.2012.02.053","volume":"39","author":"SJ Delany","year":"2012","unstructured":"Delany, S.J., Buckley, M., Greene, D.: SMS spam filtering: methods and data. Expert Syst. Appl. 39(10), 9899\u20139908 (2012)","journal-title":"Expert Syst. Appl."},{"issue":"1","key":"6_CR3","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1109\/MTAS.2006.1607720","volume":"25","author":"B Hoanca","year":"2006","unstructured":"Hoanca, B.: How good are our weapons in the spam wars? IEEE Technol. Soc. Mag. 25(1), 22\u201330 (2006)","journal-title":"IEEE Technol. Soc. Mag."},{"key":"6_CR4","doi-asserted-by":"publisher","first-page":"421","DOI":"10.1016\/j.ins.2014.02.114","volume":"277","author":"C Laorden","year":"2014","unstructured":"Laorden, C., Ugarte-Pedrero, X., Santos, I., Sanz, B., Nieves, J., Bringas, P.G.: Study on the effectiveness of anomaly detection for spam filtering. Inf. Sci. 277, 421\u2013444 (2014)","journal-title":"Inf. Sci."},{"issue":"11","key":"6_CR5","doi-asserted-by":"publisher","first-page":"2743","DOI":"10.1109\/TC.2013.152","volume":"63","author":"H Shen","year":"2014","unstructured":"Shen, H., Li, Z.: Leveraging social networks for effective spam filtering. IEEE Trans. Comput. 63(11), 2743\u20132759 (2014)","journal-title":"IEEE Trans. Comput."},{"key":"6_CR6","doi-asserted-by":"crossref","unstructured":"Androutsopoulos, I., Koutsias, J., Chandrinos, K.V., Spyropoulos, C.D.: An experimental comparison of naive bayesian and keyword-based anti-spam filtering with personal E-mail messages. In: Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 160\u2013167. ACM (2000)","DOI":"10.1145\/345508.345569"},{"key":"6_CR7","unstructured":"Metsis, V., Androutsopoulos, I., Paliouras, G.: Spam filtering with naive bayes - which naive bayes? In: Third Conference on Email and AntiSpam (CEAS), pp. 27\u201328 (2006)"},{"key":"6_CR8","unstructured":"Carreras, X., Marquez, L.: Boosting trees for anti-spam email filtering. In: Proceedings of RANLP 2001, Bulgaria, pp. 58\u201364 (2001)"},{"issue":"5","key":"6_CR9","doi-asserted-by":"publisher","first-page":"1048","DOI":"10.1109\/72.788645","volume":"10","author":"H Drucker","year":"1999","unstructured":"Drucker, H., Wu, D., Vapnik, V.: Support vector machines for spam categorization. IEEE Trans. Neural Netw. 10(5), 1048\u20131054 (1999)","journal-title":"IEEE Trans. Neural Netw."},{"issue":"1","key":"6_CR10","doi-asserted-by":"publisher","first-page":"1503","DOI":"10.1016\/j.eswa.2011.08.040","volume":"39","author":"S Jiang","year":"2012","unstructured":"Jiang, S., Pang, G., Wu, M., Kuang, L.: An Improved K-nearest-neighbor algorithm for text categorization. Expert Syst. Appl. 39(1), 1503\u20131509 (2012)","journal-title":"Expert Syst. Appl."},{"key":"6_CR11","doi-asserted-by":"crossref","unstructured":"Clark, J., Koprinska, I., Poon, J.: A neural network based approach to automated e-mail classification. In: Proceedings of the IEEE\/WIC International Conference on Web Intelligence (WI 2003), pp. 702\u2013705. IEEE Computer Society (2003)","DOI":"10.1109\/WI.2003.1241300"},{"issue":"1","key":"6_CR12","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1007\/s10844-013-0254-7","volume":"42","author":"B Zhou","year":"2014","unstructured":"Zhou, B., Yao, Y., Luo, J.: Cost-sensitive three-way email spam filtering. J. Intell. Inf. Syst. 42(1), 19\u201345 (2014)","journal-title":"J. Intell. Inf. Syst."},{"issue":"7","key":"6_CR13","doi-asserted-by":"publisher","first-page":"10206","DOI":"10.1016\/j.eswa.2009.02.037","volume":"36","author":"T Guzella","year":"2009","unstructured":"Guzella, T., Caminhas, W.: A review of machine learning approaches to spam filtering. Expert Syst. Appl. 36(7), 10206\u201310222 (2009)","journal-title":"Expert Syst. Appl."},{"issue":"2","key":"6_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2089125.2089129","volume":"44","author":"G Caruana","year":"2012","unstructured":"Caruana, G., Li, M.: A survey of emerging approaches to spam filtering. ACM Comput. Surv. 44(2), 1\u201327 (2012)","journal-title":"ACM Comput. Surv."},{"key":"6_CR15","doi-asserted-by":"crossref","first-page":"437","DOI":"10.1007\/978-3-662-44851-9_28","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"J Nam","year":"2014","unstructured":"Nam, J., Kim, J., Menc\u00eda, E.L., Gurevych, I., F\u00fcrnkranz, J.: Large-scale multi-label text classification - revisiting neural networks. In: Calders, T., Esposito, F., H\u00fcllermeier, E., Melo, R. (eds.) Machine Learning and Knowledge Discovery in Databases, pp. 437\u2013452. Springer, Berlin Heidelberg (2014)"},{"key":"6_CR16","unstructured":"Hinton, G., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Improving neural networks by preventing co-adaptation of feature detectors. arXiv:1207.0580 (2012)"},{"issue":"1","key":"6_CR17","first-page":"4","volume":"1","author":"A Khan","year":"2010","unstructured":"Khan, A., Baharudin, B., Lee, L.: A review of machine learning algorithms for text-documents classification. J. Adv. Inf. Technol. 1(1), 4\u201320 (2010)","journal-title":"J. Adv. Inf. Technol."},{"issue":"8","key":"6_CR18","doi-asserted-by":"publisher","first-page":"566","DOI":"10.1016\/j.cose.2006.06.001","volume":"25","author":"J Carpinter","year":"2006","unstructured":"Carpinter, J., Hunt, R.: Tightening the net: a review of current and next generation spam filtering tools. Comput. Secur. 25(8), 566\u2013578 (2006)","journal-title":"Comput. Secur."},{"issue":"3","key":"6_CR19","first-page":"28","volume":"111","author":"D Talbot","year":"2008","unstructured":"Talbot, D.: Where Spam is born. MIT Technol. Rev. 111(3), 28 (2008)","journal-title":"MIT Technol. Rev."},{"issue":"2","key":"6_CR20","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1145\/980972.980990","volume":"5","author":"T Fawcett","year":"2003","unstructured":"Fawcett, T.: In vivo spam filtering: a challenge problem for KDD. ACM SIGKDD Explor. Newsl. 5(2), 140\u2013148 (2003)","journal-title":"ACM SIGKDD Explor. Newsl."},{"key":"6_CR21","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1016\/j.knosys.2014.03.015","volume":"64","author":"Y Zhang","year":"2014","unstructured":"Zhang, Y., Wang, S., Phillips, P., Ji, G.: Binary PSO with mutation operator for feature selection using decision tree applied to spam detection. Knowl.-Based Syst. 64, 22\u201331 (2014)","journal-title":"Knowl.-Based Syst."},{"key":"6_CR22","unstructured":"Sahami, M., Dumais, S., Heckerman, D., Horvitz, E.: A bayesian approach to filtering junk E-Mail. In: Papers from the 1998 Workshop Learning for Text Categorization, vol. 62, pp. 98\u2013105 (1998)"},{"issue":"4","key":"6_CR23","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1145\/1039621.1039625","volume":"3","author":"L Zhang","year":"2004","unstructured":"Zhang, L., Zhu, J., Yao, T.: An evaluation of statistical spam filtering techniques. ACM Trans. Asian Lang. Inf. Process. 3(4), 243\u2013269 (2004)","journal-title":"ACM Trans. Asian Lang. Inf. Process."},{"issue":"10","key":"6_CR24","doi-asserted-by":"publisher","first-page":"2167","DOI":"10.1016\/j.ins.2006.12.005","volume":"177","author":"I Koprinska","year":"2007","unstructured":"Koprinska, I., Poon, J., Clark, J., Chan, J.: Learning to classify E-mail. Inf. Sci. 177(10), 2167\u20132187 (2007)","journal-title":"Inf. Sci."},{"issue":"3","key":"6_CR25","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1016\/j.knosys.2006.05.016","volume":"20","author":"C Lai","year":"2007","unstructured":"Lai, C.: An empirical study of three machine learning methods for spam filtering. Knowl.-Based Syst. 20(3), 249\u2013254 (2007)","journal-title":"Knowl.-Based Syst."},{"key":"6_CR26","doi-asserted-by":"crossref","unstructured":"Vyas, T., Prajapati, P., Gadhwal, S.: A survey and evaluation of supervised machine learning techniques for spam E-mail filtering. In: IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT), pp. 1\u20137. IEEE (2015)","DOI":"10.1109\/ICECCT.2015.7226077"},{"key":"6_CR27","doi-asserted-by":"crossref","unstructured":"Almeida, T.A., Hidalgo, J.M.G., Yamakami, A.: Contributions to the study of SMS spam filtering: new collection and results. In: Proceedings of the 11th ACM Symposium on Document Engineering, pp. 259\u2013262. ACM (2011)","DOI":"10.1145\/2034691.2034742"},{"key":"6_CR28","unstructured":"Maas, A.L., Hannun, A.Y., Ng, A.Y.: Rectifier nonlinearities improve neural network acoustic models. In: Proceedings of the 30th International Conference on Machine Learning, vol. 30, pp. 1\u20136 (2013)"},{"key":"6_CR29","doi-asserted-by":"crossref","unstructured":"Jaitly, N., Hinton, G.: Learning a better representation of speech soundwaves using restricted boltzmann machines. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5884\u20135887. IEEE (2011)","DOI":"10.1109\/ICASSP.2011.5947700"},{"key":"6_CR30","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1007\/978-3-319-44188-7_5","volume-title":"Engineering Applications of Neural Networks (EANN)","author":"P Hajek","year":"2016","unstructured":"Hajek, P., Bohacova, J.: Predicting abnormal bank stock returns using textual analysis of annual reports - a neural network approach. In: Jayne, C., Iliadis, L. (eds.) Engineering Applications of Neural Networks (EANN), pp. 67\u201378. Springer, New York (2016)"}],"container-title":["Lecture Notes in Computer Science","AI*IA 2016 Advances in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-49130-1_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,12]],"date-time":"2025-06-12T00:15:12Z","timestamp":1749687312000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-319-49130-1_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016]]},"ISBN":["9783319491295","9783319491301"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-49130-1_6","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2016]]},"assertion":[{"value":"5 November 2016","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AI*IA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Conference of the Italian Association for Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Genova","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2016","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 November 2016","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 December 2016","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"XV","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aiia2016","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.aixia2016.unige.it\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}