{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,20]],"date-time":"2025-10-20T10:25:51Z","timestamp":1760955951176,"version":"3.40.3"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030210762"},{"type":"electronic","value":"9783030210779"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"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":[[2019]]},"DOI":"10.1007\/978-3-030-21077-9_9","type":"book-chapter","created":{"date-parts":[[2019,6,18]],"date-time":"2019-06-18T23:14:41Z","timestamp":1560899681000},"page":"89-101","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["An Improved Convolutional Neural Network Architecture for Image Classification"],"prefix":"10.1007","author":[{"given":"A.","family":"Ferreyra-Ramirez","sequence":"first","affiliation":[]},{"given":"C.","family":"Aviles-Cruz","sequence":"additional","affiliation":[]},{"given":"E.","family":"Rodriguez-Martinez","sequence":"additional","affiliation":[]},{"given":"J.","family":"Villegas-Cortez","sequence":"additional","affiliation":[]},{"given":"A.","family":"Zu\u00f1iga-Lopez","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,5,18]]},"reference":[{"key":"9_CR1","unstructured":"Caltech-101 dataset. http:\/\/www.vision.caltech.edu\/Image_Datasets\/Caltech101"},{"key":"9_CR2","unstructured":"Caltech-256 dataset. http:\/\/www.vision.caltech.edu\/Image_Datasets\/Caltech256"},{"key":"9_CR3","unstructured":"Imagenet dataset. http:\/\/www.image-net.org\/"},{"key":"9_CR4","unstructured":"Labelme dataset. http:\/\/cvcl.mit.edu\/database.html"},{"key":"9_CR5","unstructured":"The Oxford Buildings dataset. http:\/\/www.robots.ox.ac.uk\/~vgg\/data\/oxbuildings"},{"key":"9_CR6","unstructured":"The Paris dataset. http:\/\/www.robots.ox.ac.uk\/~vgg\/data\/parisbuildings"},{"issue":"3","key":"9_CR7","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1023\/A:1011139631724","volume":"42","author":"A Oliva","year":"2001","unstructured":"Oliva, A., Torralba, A.: Modeling the shape of the scene: a holistic representation of the spatial envelop. Int. J. Comput. Vis. 42(3), 145\u2013175 (2001)","journal-title":"Int. J. Comput. Vis."},{"key":"9_CR8","volume-title":"Neural Networks for Pattern Recognition","author":"CM Bishop","year":"1996","unstructured":"Bishop, C.M.: Neural Networks for Pattern Recognition. Clarendon Press, Gloucestershire (1996)"},{"key":"9_CR9","doi-asserted-by":"crossref","unstructured":"Burges, C., et al.: Learning to rank using gradient descent. In: 22nd International conference on Machine Learning. ACM Press (2005)","DOI":"10.1145\/1102351.1102363"},{"key":"9_CR10","doi-asserted-by":"crossref","unstructured":"Chatfield, K., Simonyan, K., Vedaldi, A., Zisserman, A.: Return of the devil in the details: delving deep into convolutional nets. In: proceedings of ECCV (2014)","DOI":"10.5244\/C.28.6"},{"key":"9_CR11","unstructured":"Ciresan, D.C., Meier, U., Masci, J., Gambardella, L., Schmidhuber, J.: Flexible high performance convolutional neural networks for image classification. In: Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, vol. 2, pp. 1237\u20131242 (2011)"},{"key":"9_CR12","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L., Li, K., Li, F.F.: ImageNet: a large-scale hierarchical image database. In: IEEE Computer Vision and Pattern Recognition, pp. 248\u2013255 (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"issue":"4","key":"9_CR13","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1007\/BF00344251","volume":"36","author":"K Fukushima","year":"1980","unstructured":"Fukushima, K.: Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol. Cybern. 36(4), 193\u2013202 (1980)","journal-title":"Biol. Cybern."},{"key":"9_CR14","doi-asserted-by":"crossref","unstructured":"Girshick, R.B., Danahue, J.: Rich features hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580\u2013587 (2014)","DOI":"10.1109\/CVPR.2014.81"},{"key":"9_CR15","unstructured":"Glorot, X., Bordes, A., Bengio, Y.: Deep sparse rectifier neural networks. In: Proceedings of the International Conference on Artificial Intelligence and Statistics, vol. 15, pp. 315\u2013323 (2011)"},{"key":"9_CR16","volume-title":"Deep Learning","author":"I Goodfellow","year":"2016","unstructured":"Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. The MIT Press, Cambridge (2016)"},{"key":"9_CR17","doi-asserted-by":"crossref","unstructured":"Hertel, L., Barth, E., Kaster, T., Martinetz, T.: Deep convolutional neural networks as generic feature extractor. In: Proceedings of the IEEE International Joint Conference on Neural Networks, pp. 1\u20134, July 2015","DOI":"10.1109\/IJCNN.2015.7280683"},{"key":"9_CR18","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, vol. 25, pp. 1097\u20131105 (2012)"},{"key":"9_CR19","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86, 2278\u20132324 (1998)","journal-title":"Proc. IEEE"},{"key":"9_CR20","unstructured":"Lin, M., Chen, Q., Yan, S.: Network in network. In: Proceedings of the 2nd International Conference on Learning Representation (2014)"},{"key":"9_CR21","doi-asserted-by":"crossref","unstructured":"Razavjan, A.S., Azizpour, H., Sullivan, J., Carlsson, S.: CNN features off-the-shelf: an astounding baseline for recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 512\u2013519 (2014)","DOI":"10.1109\/CVPRW.2014.131"},{"key":"9_CR22","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to precent neural network from overfitting. J. Mach. Learn. Res. 15, 1929\u20131958 (2014)","journal-title":"J. Mach. Learn. Res."},{"key":"9_CR23","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1016\/j.imavis.2016.11.012","volume":"60","author":"W Sun","year":"2017","unstructured":"Sun, W., Su, F.: A novel companion objective function for regularization of deep convolutional neural networks. Image Vis. Comput. 60, 58\u201363 (2017)","journal-title":"Image Vis. Comput."},{"key":"9_CR24","doi-asserted-by":"crossref","unstructured":"Usunier, N., Buffoni, D., Gallinari, P.: Ranking with ordered weighted pairwise classification. In: Proceedings of the 26th Annual International Conference on Machine Learning, ACM Press (2009)","DOI":"10.1145\/1553374.1553509"},{"key":"9_CR25","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1007\/978-3-319-68612-7_6","volume-title":"Artificial Neural Networks and Machine Learning \u2013 ICANN 2017","author":"B Wu","year":"2017","unstructured":"Wu, B., Liu, Z., Yuan, Z., Sun, G., Wu, C.: Reducing overfitting in deep convolutional neural networks using redundancy regularizer. In: Lintas, A., Rovetta, S., Verschure, P.F.M.J., Villa, A.E.P. (eds.) ICANN 2017. LNCS, vol. 10614, pp. 49\u201355. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-68612-7_6"},{"issue":"12","key":"9_CR26","doi-asserted-by":"publisher","first-page":"2273","DOI":"10.1109\/TCSVT.2015.2477937","volume":"26","author":"C Xu","year":"2016","unstructured":"Xu, C., et al.: Multi-loss regularized deep neural network. IEEE Trans. Circ. Syst. Video Technol. 26(12), 2273\u20132283 (2016)","journal-title":"IEEE Trans. Circ. Syst. Video Technol."},{"key":"9_CR27","unstructured":"Yosinski, J., Clune, J., Bengio, Y.: How transferable are features in deep neural networks? In: Advances in Neural Information Processing Systems, vol. 27, pp. 3320\u20133328 (2014)"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-21077-9_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,19]],"date-time":"2023-06-19T00:05:27Z","timestamp":1687133127000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-21077-9_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030210762","9783030210779"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-21077-9_9","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"18 May 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MCPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Mexican Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Quer\u00e9taro","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Mexico","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":"29 June 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mcpr22019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.mcpr.org.mx","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"86","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"40","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"47% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.82","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.39","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}