{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T11:59:31Z","timestamp":1774871971147,"version":"3.50.1"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783319751924","type":"print"},{"value":"9783319751931","type":"electronic"}],"license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018]]},"DOI":"10.1007\/978-3-319-75193-1_18","type":"book-chapter","created":{"date-parts":[[2018,2,3]],"date-time":"2018-02-03T11:02:59Z","timestamp":1517655779000},"page":"143-151","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Efficient Hyperparameter Optimization in Convolutional Neural Networks by Learning Curves Prediction"],"prefix":"10.1007","author":[{"given":"Andr\u00e9s F.","family":"Cardona-Escobar","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andr\u00e9s F.","family":"Giraldo-Forero","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andr\u00e9s E.","family":"Castro-Ospina","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jorge A.","family":"Jaramillo-Garz\u00f3n","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2018,2,4]]},"reference":[{"key":"18_CR1","doi-asserted-by":"publisher","first-page":"1660","DOI":"10.1109\/ACCESS.2015.2389854","volume":"2","author":"C Perera","year":"2014","unstructured":"Perera, C., Liu, C.H., Jayawardena, S., Chen, M.: A survey on internet of things from industrial market perspective. IEEE Access 2, 1660\u20131679 (2014)","journal-title":"IEEE Access"},{"key":"18_CR2","first-page":"281","volume":"13","author":"J Bergstra","year":"2012","unstructured":"Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. J. Mach. Learn. Res. 13, 281\u2013305 (2012)","journal-title":"J. Mach. Learn. Res."},{"key":"18_CR3","unstructured":"Snoek, J., Larochelle, H., Adams, R.P.: Practical Bayesian optimization of machine learning algorithms. In: Advances in Neural Information Processing Systems (2012)"},{"key":"18_CR4","unstructured":"Domhan, T., Springenberg, J.T., Hutter, F.: Speeding up automatic hyperparameter optimization of deep neural networks by extrapolation of learning curves"},{"key":"18_CR5","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4757-3264-1","volume-title":"The Nature of Statistical Learning Theory","author":"V Vapnik","year":"2013","unstructured":"Vapnik, V.: The Nature of Statistical Learning Theory. Springer Science & Business Media, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-1-4757-3264-1"},{"key":"18_CR6","unstructured":"Tao, D.: The COREL database for content based image retrieval. https:\/\/sites.google.com\/site\/dctresearch\/Home\/content-based-image-retrieval"},{"key":"18_CR7","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: NIPS, pp. 1097\u20131105 (2012)"},{"key":"18_CR8","doi-asserted-by":"crossref","unstructured":"Graves, A., Mohamed, A., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6645\u20136649. IEEE (2013)","DOI":"10.1109\/ICASSP.2013.6638947"},{"issue":"1","key":"18_CR9","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1109\/TCBB.2014.2343960","volume":"12","author":"M Spencer","year":"2015","unstructured":"Spencer, M., Eickholt, J., Cheng, J.: A deep learning network approach to ab initio protein secondary structure prediction. IEEE\/ACM Trans. Comput. Biol. Bioinf. 12(1), 103\u2013112 (2015)","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinf."},{"key":"18_CR10","unstructured":"Busia, A., Collins, J., Jaitly, N.: Protein secondary structure prediction using deep multi-scale convolutional neural networks and next-step conditioning. arXiv preprint arXiv:1611.01503 (2016)"},{"key":"18_CR11","doi-asserted-by":"crossref","unstructured":"Golyandina, N., Nekrutkin, V., Zhigljavsky, A.: Analysis of time series structure: SSA and related techniques (2001)","DOI":"10.1201\/9781420035841"},{"issue":"8","key":"18_CR12","doi-asserted-by":"publisher","first-page":"5040","DOI":"10.1109\/TIT.2014.2323359","volume":"60","author":"M Gavish","year":"2014","unstructured":"Gavish, M., Donoho, D.L.: The optimal hard threshold for singular values is $$4\/sqrt(3)$$. IEEE Trans. Inf. Theory 60(8), 5040\u20135053 (2014)","journal-title":"IEEE Trans. Inf. Theory"},{"key":"18_CR13","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1016\/j.neucom.2012.06.030","volume":"99","author":"L Zhang","year":"2013","unstructured":"Zhang, L., Zhou, W.D., Chang, P.C., et al.: Iterated time series prediction with multiple support vector regression models. Neurocomputing 99, 411\u2013422 (2013)","journal-title":"Neurocomputing"},{"key":"18_CR14","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011)","journal-title":"J. Mach. Learn. Res."},{"key":"18_CR15","unstructured":"LeCun, Y.: The MNIST database of handwritten digits (1998)"},{"issue":"9","key":"18_CR16","doi-asserted-by":"publisher","first-page":"947","DOI":"10.1109\/34.955109","volume":"23","author":"JZ Wang","year":"2001","unstructured":"Wang, J.Z., Li, J., Wiederhold, G.: Simplicity: semantics-sensitive integrated matching for picture libraries. IEEE Trans. Pattern Anal. Mach. Intell. 23(9), 947\u2013963 (2001)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"18_CR17","doi-asserted-by":"crossref","unstructured":"Acharya, S., Pant, A.K., Gyawali, P.K.: Deep learning based large scale handwritten Devanagari character recognition. In: 2015 9th International Conference on Software, Knowledge, Information Management and Applications (SKIMA), pp. 1\u20136. IEEE (2015)","DOI":"10.1109\/SKIMA.2015.7400041"},{"issue":"7","key":"18_CR18","doi-asserted-by":"publisher","first-page":"1088","DOI":"10.1109\/TPAMI.2006.134","volume":"28","author":"D Tao","year":"2006","unstructured":"Tao, D., Tang, X., Li, X., Wu, X.: Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 28(7), 1088\u20131099 (2006)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"18_CR19","unstructured":"Chollet, F.: Keras (2015). https:\/\/github.com\/fchollet\/keras.git"},{"key":"18_CR20","unstructured":"Abadi, M., Agarwal, A., et al.: TensorFlow: large-scale machine learning on heterogeneous systems (2015). https:\/\/www.tensorflow.org\/"}],"container-title":["Lecture Notes in Computer Science","Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-75193-1_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T10:18:13Z","timestamp":1710325093000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-319-75193-1_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783319751924","9783319751931"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-75193-1_18","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018]]},"assertion":[{"value":"4 February 2018","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CIARP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Iberoamerican Congress on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Valpara\u00edso","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chile","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2017","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 November 2017","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 November 2017","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ciarp2017","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ciarp2017.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}