{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,3,22]],"date-time":"2024-03-22T10:00:59Z","timestamp":1711101659101},"reference-count":27,"publisher":"World Scientific Pub Co Pte Lt","issue":"04","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2003,6]]},"abstract":"<jats:p> Traditional pattern recognition approaches usually generalize poorly on difficult tasks as the problem of identification of the Seismic Electric Signals (SES) electrotelluric precursors for earthquake prediction. This work demonstrates that the Support Vector Machine (SVM) can perform well on this application. The a priori knowledge consists of a set of VAN rules for SES signal detection. The SVM extracts implicitly these rules from properly preprocessed features and obtains generalization performance founded upon a robust mathematical basis. The potentiality of obtaining generalization potential even in feature spaces of high dimensionality bypasses the problems due to overtraining of the conventional machine learning architectures. The paper considers the optimization of the generalization performance of the SVM. The results indicate that the SVM outperforms many alternative computational intelligence models for the task of SES pattern recognition. <\/jats:p>","DOI":"10.1142\/s0218001403002484","type":"journal-article","created":{"date-parts":[[2003,6,18]],"date-time":"2003-06-18T07:52:56Z","timestamp":1055922776000},"page":"545-565","source":"Crossref","is-referenced-by-count":2,"title":["SUPPORT VECTOR IDENTIFICATION OF SEISMIC ELECTRIC SIGNALS"],"prefix":"10.1142","volume":"17","author":[{"given":"A.","family":"IFANTIS","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, Technological Educational Institute of Patras, Patras 26334, Greece"},{"name":"Seismological Lab., Geology Department,  University of Patras, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"S.","family":"PAPADIMITRIOU","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Technological Educational Institute of Patras, Patras 26334, Greece"},{"name":"Department of Information Management, Technological Educational Institute of Kavala, Kavala 65404, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2011,11,21]]},"reference":[{"key":"rf1","unstructured":"P.\u00a0Bartlett and J. S.\u00a0Taylor, Advances in Kernel Methods, Support Vector Learning (The MIT Press, 1999)\u00a0pp. 43\u201355."},{"key":"rf2","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.97.1.262"},{"key":"rf3","first-page":"1","volume":"20","author":"Cortes C.","journal-title":"Mach. Learn."},{"key":"rf4","doi-asserted-by":"publisher","DOI":"10.1162\/089976698300017269"},{"key":"rf6","doi-asserted-by":"publisher","DOI":"10.1007\/s005290050032"},{"key":"rf7","doi-asserted-by":"publisher","DOI":"10.1109\/21.256541"},{"key":"rf8","volume-title":"Neural Networks","author":"Haykin S.","year":"1999"},{"key":"rf9","unstructured":"T.\u00a0Joachims, Advances in Kernel Methods \u2014 Support Vector Learning, eds. B.\u00a0Scholkopf, C. J. C.\u00a0Burges and A. J.\u00a0Smola (MIT Press, Cambridge, USA, 1998)\u00a0pp. 169\u2013184."},{"key":"rf11","doi-asserted-by":"publisher","DOI":"10.1016\/0040-1951(93)90059-S"},{"key":"rf12","volume-title":"Fuzzy Enginnering","author":"Kosko B.","year":"1997"},{"key":"rf13","first-page":"614","volume":"11","author":"Mallat S.","journal-title":"IEEE Trans. Patt. Anal. Mach. Intell."},{"key":"rf14","doi-asserted-by":"publisher","DOI":"10.1109\/34.142909"},{"key":"rf15","doi-asserted-by":"publisher","DOI":"10.1109\/18.119727"},{"key":"rf16","unstructured":"D.\u00a0Mattera and S.\u00a0Haykin, Advances in Kernel Methods \u2014 Support Vector Learning, eds. B.\u00a0Scholkopf, J.\u00a0Burges and A. J.\u00a0Smola (MIT Press, Cambridge, MA, 1999)\u00a0pp. 211\u2013242."},{"key":"rf17","doi-asserted-by":"publisher","DOI":"10.1109\/72.788643"},{"key":"rf18","doi-asserted-by":"publisher","DOI":"10.1137\/0801008"},{"key":"rf19","first-page":"830","volume":"30","author":"Yu M.","journal-title":"Phys. Solid Earth"},{"key":"rf21","doi-asserted-by":"publisher","DOI":"10.1109\/72.925554"},{"key":"rf22","doi-asserted-by":"publisher","DOI":"10.1109\/72.788641"},{"key":"rf24","doi-asserted-by":"publisher","DOI":"10.1029\/96GL01477"},{"key":"rf25","doi-asserted-by":"publisher","DOI":"10.1109\/72.857766"},{"key":"rf26","volume-title":"Statistical Learning Theory","author":"Vapnik V. N.","year":"1998"},{"key":"rf27","doi-asserted-by":"publisher","DOI":"10.1109\/72.788640"},{"key":"rf28","unstructured":"V. N.\u00a0Vapnik, Advances in Kernel Methods, Support Vector Learning (The MIT Press, 1999)\u00a0pp. 25\u201341."},{"key":"rf29","first-page":"425","author":"Varotsos P.","journal-title":"Acta Geophys. Polonica"},{"key":"rf30","doi-asserted-by":"publisher","DOI":"10.1016\/0040-1951(84)90059-3"},{"key":"rf31","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/16.9.799"}],"container-title":["International Journal of Pattern Recognition and Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S0218001403002484","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,6]],"date-time":"2019-08-06T22:21:03Z","timestamp":1565130063000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/abs\/10.1142\/S0218001403002484"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2003,6]]},"references-count":27,"journal-issue":{"issue":"04","published-online":{"date-parts":[[2011,11,21]]},"published-print":{"date-parts":[[2003,6]]}},"alternative-id":["10.1142\/S0218001403002484"],"URL":"https:\/\/doi.org\/10.1142\/s0218001403002484","relation":{},"ISSN":["0218-0014","1793-6381"],"issn-type":[{"value":"0218-0014","type":"print"},{"value":"1793-6381","type":"electronic"}],"subject":[],"published":{"date-parts":[[2003,6]]}}}