{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T19:59:33Z","timestamp":1760385573606,"version":"3.37.3"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319677859"},{"type":"electronic","value":"9783319677866"}],"license":[{"start":{"date-parts":[[2017,1,1]],"date-time":"2017-01-01T00:00:00Z","timestamp":1483228800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017]]},"DOI":"10.1007\/978-3-319-67786-6_10","type":"book-chapter","created":{"date-parts":[[2017,9,15]],"date-time":"2017-09-15T04:37:22Z","timestamp":1505450242000},"page":"127-143","source":"Crossref","is-referenced-by-count":3,"title":["Re-training Deep Neural Networks to Facilitate Boolean Concept Extraction"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4510-7309","authenticated-orcid":false,"given":"Camila","family":"Gonz\u00e1lez","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eneldo","family":"Loza Menc\u00eda","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1207-0159","authenticated-orcid":false,"given":"Johannes","family":"F\u00fcrnkranz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2017,9,16]]},"reference":[{"key":"10_CR1","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4757-3115-6","volume-title":"Multi-valued and Universal Binary Neurons: Theory, Learning and Applications","author":"I Aizenberg","year":"2013","unstructured":"Aizenberg, I., Aizenberg, N.N., Vandewalle, J.P.: Multi-valued and Universal Binary Neurons: Theory, Learning and Applications. Springer, New York (2013). doi:\n10.1007\/978-1-4757-3115-6"},{"issue":"6","key":"10_CR2","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1016\/0950-7051(96)81920-4","volume":"8","author":"R Andrews","year":"1995","unstructured":"Andrews, R., Diederich, J., Tickle, A.B.: Survey and critique of techniques for extracting rules from trained artificial neural networks. Knowl. Based Syst. 8(6), 373\u2013389 (1995)","journal-title":"Knowl. Based Syst."},{"key":"10_CR3","unstructured":"Courbariaux, M., Bengio, Y., David, J.: BinaryConnect: training deep neural networks with binary weights during propagations. In: Advances in Neural Information Processing Systems 28 (NIPS 2015), Montreal, Quebec, Canada, pp. 3123\u20133131 (2015)"},{"key":"10_CR4","doi-asserted-by":"crossref","unstructured":"Craven, M., Shavlik, J.W.: Using sampling and queries to extract rules from trained neural networks. In: Proceedings of the 11th International Conference on Machine Learning (ICML 1994), pp. 37\u201345. Morgan Kaufmann, New Brunswick (1994)","DOI":"10.1016\/B978-1-55860-335-6.50013-1"},{"key":"10_CR5","unstructured":"Craven, M., Shavlik, J.W.: Extracting tree-structured representations of trained networks. In: Advances in Neural Information Processing Systems 8 (NIPS 1995), pp. 24\u201330 (1995)"},{"issue":"1","key":"10_CR6","first-page":"1","volume":"7","author":"J Dem\u0161ar","year":"2006","unstructured":"Dem\u0161ar, J., Schuurmans, D.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7(1), 1\u201330 (2006)","journal-title":"J. Mach. Learn. Res."},{"issue":"1","key":"10_CR7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2594473.2594475","volume":"15","author":"AA Freitas","year":"2013","unstructured":"Freitas, A.A.: Comprehensible classification models: a position paper. SIGKDD Explor. 15(1), 1\u201310 (2013)","journal-title":"SIGKDD Explor."},{"key":"10_CR8","unstructured":"Fu, L.: Rule learning by searching on adapted nets. In: Proceedings of the 9th National Conference on Artificial Intelligence (AAAI 1991), Anaheim, CA, USA, vol. 2, pp. 590\u2013595 (1991)"},{"key":"10_CR9","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-75197-7","volume-title":"Foundations of Rule Learning","author":"J F\u00fcrnkranz","year":"2012","unstructured":"F\u00fcrnkranz, J., Gamberger, D., Lavra\u010d, N.: Foundations of Rule Learning. Springer, Heidelberg (2012). doi:\n10.1007\/978-3-540-75197-7"},{"key":"10_CR10","series-title":"Adaptive Computation and Machine Learning","volume-title":"Deep Learning","author":"IJ Goodfellow","year":"2016","unstructured":"Goodfellow, I.J., Bengio, Y., Courville, A.C.: Deep Learning. Adaptive Computation and Machine Learning. MIT Press, Cambridge (2016)"},{"key":"10_CR11","unstructured":"Han, S., Pool, J., Tran, J., Dally, W.J.: Learning both weights and connections for efficient neural network. In: Advances in Neural Information Processing Systems 28 (NIPS 2015), Montreal, Quebec, Canada, pp. 1135\u20131143 (2015)"},{"key":"10_CR12","unstructured":"Hassibi, B., Stork, D.G.: Second order derivatives for network pruning: optimal brain surgeon. In: Advances in Neural Information Processing Systems 5 (NIPS 1992), pp. 164\u2013171. Morgan Kaufmann, Denver (1992)"},{"key":"10_CR13","volume-title":"Digital Logic Design","author":"JP Hayes","year":"1993","unstructured":"Hayes, J.P.: Digital Logic Design. Addison Wesley, Reading (1993)"},{"issue":"4","key":"10_CR14","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1287\/isre.1080.0198","volume":"20","author":"U Kayande","year":"2009","unstructured":"Kayande, U., Bruyn, A.D., Lilien, G.L., Rangaswamy, A., van Bruggen, G.H.: How incorporating feedback mechanisms in a DSS affects DSS evaluations. Inf. Syst. Res. 20(4), 527\u2013546 (2009)","journal-title":"Inf. Syst. Res."},{"key":"10_CR15","unstructured":"LeCun, Y., Denker, J.S., Solla, S.A.: Optimal brain damage. In: Advances in Neural Information Processing Systems 2 (NIPS 1990), Denver, Colorado, USA, pp. 598\u2013605 (1989)"},{"issue":"7","key":"10_CR16","doi-asserted-by":"crossref","first-page":"971","DOI":"10.1089\/cmb.2005.12.971","volume":"12","author":"J Liu","year":"2005","unstructured":"Liu, J., Li, M.: Finding cancer biomarkers from mass spectrometry data by decision lists. J. Comput. Biol. 12(7), 971\u2013979 (2005)","journal-title":"J. Comput. Biol."},{"key":"10_CR17","unstructured":"Malioutov, D.M., Varshney, K.R.: Exact rule learning via Boolean compressed sensing. In: Proceedings of the 30th International Conference on Machine Learning (ICML 2013), Atlanta, GA, USA, pp. 765\u2013773 (2013)"},{"key":"10_CR18","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"272","DOI":"10.1007\/3-540-46016-0_29","volume-title":"MICAI 2002: Advances in Artificial Intelligence","author":"CR Milar\u00e9","year":"2002","unstructured":"Milar\u00e9, C.R., Carvalho, A.C.P.L.F., Monard, M.C.: Extracting knowledge from artificial neural networks: an empirical comparison of trepan and symbolic learning algorithms. In: Coello Coello, C.A., Albornoz, A., Sucar, L.E., Battistutti, O.C. (eds.) MICAI 2002. LNCS (LNAI), vol. 2313, pp. 272\u2013281. Springer, Heidelberg (2002). doi:\n10.1007\/3-540-46016-0_29"},{"key":"10_CR19","unstructured":"Ng, A.: Sparse autoencoder. CS294A Lecture Notes, Stanford University (2011)"},{"key":"10_CR20","doi-asserted-by":"crossref","unstructured":"Sato, M., Tsukimoto, H.: Rule extraction from neural networks via decision tree induction. In: Proceedings of the International Joint Conference on Neural Networks (IJCNN 2001), vol. 3, pp. 1870\u20131875. IEEE Press (2001)","DOI":"10.1109\/IJCNN.2001.938448"},{"issue":"1","key":"10_CR21","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/0933-3657(95)00019-4","volume":"8","author":"R Setiono","year":"1996","unstructured":"Setiono, R.: Extracting rules from pruned neural networks for breast cancer diagnosis. Artif. Intell. Med. 8(1), 37\u201351 (1996)","journal-title":"Artif. Intell. Med."},{"issue":"1","key":"10_CR22","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1162\/neco.1997.9.1.205","volume":"9","author":"R Setiono","year":"1997","unstructured":"Setiono, R.: Extracting rules from neural networks by pruning and hidden-unit splitting. Neural Comput. 9(1), 205\u2013225 (1997)","journal-title":"Neural Comput."},{"issue":"1","key":"10_CR23","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1162\/neco.1997.9.1.185","volume":"9","author":"R Setiono","year":"1997","unstructured":"Setiono, R.: A penalty-function approach for pruning feedforward neural networks. Neural Comput. 9(1), 185\u2013204 (1997)","journal-title":"Neural Comput."},{"issue":"3","key":"10_CR24","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1109\/2.485895","volume":"29","author":"R Setiono","year":"1996","unstructured":"Setiono, R., Liu, H.: Symbolic representation of neural networks. IEEE Comput. 29(3), 71\u201377 (1996)","journal-title":"IEEE Comput."},{"issue":"1","key":"10_CR25","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava, N., Hinton, G.E., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929\u20131958 (2014)","journal-title":"J. Mach. Learn. Res."},{"issue":"4","key":"10_CR26","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1142\/S0129065791000352","volume":"1","author":"HH Thodberg","year":"1991","unstructured":"Thodberg, H.H.: Improving generalization of neural networks through pruning. Int. J. Neural Syst. 1(4), 317\u2013326 (1991)","journal-title":"Int. J. Neural Syst."},{"issue":"1","key":"10_CR27","first-page":"71","volume":"13","author":"GG Towell","year":"1993","unstructured":"Towell, G.G., Shavlik, J.W.: Extracting refined rules from knowledge-based neural networks. Mach. Learn. 13(1), 71\u2013101 (1993)","journal-title":"Mach. Learn."},{"issue":"2","key":"10_CR28","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1109\/72.839008","volume":"11","author":"H Tsukimoto","year":"2000","unstructured":"Tsukimoto, H.: Extracting rules from trained neural networks. IEEE Trans. Neural Netw. Learn. Syst. 11(2), 377\u2013389 (2000)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10_CR29","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"818","DOI":"10.1007\/978-3-319-10590-1_53","volume-title":"Computer Vision \u2013 ECCV 2014","author":"MD Zeiler","year":"2014","unstructured":"Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 818\u2013833. Springer, Cham (2014). doi:\n10.1007\/978-3-319-10590-1_53"},{"key":"10_CR30","doi-asserted-by":"crossref","unstructured":"Zilke, J.R., Loza Menc\u00eda, E., Janssen, F.: DeepRED - rule extraction from deep neural networks. In: Proceedings of the 19th International Conference on Discovery Science (DS 2016), Bari, Italy, pp. 457\u2013473 (2016)","DOI":"10.1007\/978-3-319-46307-0_29"}],"container-title":["Lecture Notes in Computer Science","Discovery Science"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-67786-6_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2017,9,15]],"date-time":"2017-09-15T04:40:39Z","timestamp":1505450439000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-319-67786-6_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017]]},"ISBN":["9783319677859","9783319677866"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-67786-6_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2017]]}}}