{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T06:49:20Z","timestamp":1742971760579,"version":"3.40.3"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030926656"},{"type":"electronic","value":"9783030926663"}],"license":[{"start":{"date-parts":[[2021,12,8]],"date-time":"2021-12-08T00:00:00Z","timestamp":1638921600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,12,8]],"date-time":"2021-12-08T00:00:00Z","timestamp":1638921600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-030-92666-3_20","type":"book-chapter","created":{"date-parts":[[2021,12,7]],"date-time":"2021-12-07T10:02:54Z","timestamp":1638871374000},"page":"235-246","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Training Support Vector Machines for\u00a0Dealing with\u00a0the\u00a0ImageNet Challenging Problem"],"prefix":"10.1007","author":[{"given":"Thanh-Nghi","family":"Do","sequence":"first","affiliation":[]},{"given":"Hoai An","family":"Le Thi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,12,8]]},"reference":[{"key":"20_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"517","DOI":"10.1007\/11744085_40","volume-title":"Computer Vision \u2013 ECCV 2006","author":"A Bosch","year":"2006","unstructured":"Bosch, A., Zisserman, A., Mu\u00f1oz, X.: Scene classification Via pLSA. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part IV. LNCS, vol. 3954, pp. 517\u2013530. Springer, Heidelberg (2006). https:\/\/doi.org\/10.1007\/11744085_40"},{"key":"20_CR2","unstructured":"Bottou, L., Bousquet, O.: The tradeoffs of large scale learning. In: Platt, J., Koller, D., Singer, Y., Roweis, S. (eds.) Advances in Neural Information Processing Systems, vol.\u00a020, pp. 161\u2013168. NIPS Foundation (2008). http:\/\/books.nips.cc"},{"issue":"2","key":"20_CR3","first-page":"123","volume":"24","author":"L Breiman","year":"1996","unstructured":"Breiman, L.: Bagging predictors. Mach. Learn. 24(2), 123\u2013140 (1996)","journal-title":"Mach. Learn."},{"issue":"27","key":"20_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1961189.1961199","volume":"2","author":"CC Chang","year":"2011","unstructured":"Chang, C.C., Lin, C.J.: LIBSVM\u202f: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2(27), 1\u201327 (2011)","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"20_CR5","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1007\/978-3-540-39804-2_12","volume-title":"Knowledge Discovery in Databases: PKDD 2003","author":"NV Chawla","year":"2003","unstructured":"Chawla, N.V., Lazarevic, A., Hall, L.O., Bowyer, K.W.: SMOTEBoost: improving prediction of the minority class in boosting. In: Lavra\u010d, N., Gamberger, D., Todorovski, L., Blockeel, H. (eds.) PKDD 2003. LNCS (LNAI), vol. 2838, pp. 107\u2013119. Springer, Heidelberg (2003). https:\/\/doi.org\/10.1007\/978-3-540-39804-2_12"},{"key":"20_CR6","doi-asserted-by":"crossref","unstructured":"Chollet, F.: Xception: deep learning with depthwise separable convolutions. CoRR abs\/1610.02357 (2016)","DOI":"10.1109\/CVPR.2017.195"},{"key":"20_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1007\/978-3-642-15555-0_6","volume-title":"Computer Vision \u2013 ECCV 2010","author":"J Deng","year":"2010","unstructured":"Deng, J., Berg, A.C., Li, K., Fei-Fei, L.: What does classifying more than 10,000 image categories tell us? In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part V. LNCS, vol. 6315, pp. 71\u201384. Springer, Heidelberg (2010). https:\/\/doi.org\/10.1007\/978-3-642-15555-0_6"},{"key":"20_CR8","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Li, F.F.: Imagenet: a large-scale hierarchical image database. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 248\u2013255 (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"issue":"2","key":"20_CR9","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1007\/s40595-013-0013-2","volume":"1","author":"T-N Do","year":"2014","unstructured":"Do, T.-N.: Parallel multiclass stochastic gradient descent algorithms for classifying million images with very-high-dimensional signatures into thousands classes. Vietnam J. Comput. Sci. 1(2), 107\u2013115 (2014). https:\/\/doi.org\/10.1007\/s40595-013-0013-2","journal-title":"Vietnam J. Comput. Sci."},{"key":"20_CR10","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1007\/978-3-319-17996-4_23","volume-title":"Advanced Computational Methods for Knowledge Engineering","author":"T-N Do","year":"2015","unstructured":"Do, T.-N., Poulet, F.: Parallel multiclass logistic regression for classifying large scale image datasets. In: Le Thi, H.A., Nguyen, N.T., Do, T.V. (eds.) Advanced Computational Methods for Knowledge Engineering. AISC, vol. 358, pp. 255\u2013266. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-17996-4_23"},{"key":"20_CR11","first-page":"67","volume":"31","author":"T Do","year":"2017","unstructured":"Do, T., Poulet, F.: Parallel learning of local SVM algorithms for classifying large datasets. Trans. Large Scale Data Knowl. Centered Syst. 31, 67\u201393 (2017)","journal-title":"Trans. Large Scale Data Knowl. Centered Syst."},{"key":"20_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1007\/978-3-319-48057-2_2","volume-title":"Future Data and Security Engineering","author":"T-N Do","year":"2016","unstructured":"Do, T.-N., Tran-Nguyen, M.-T.: Incremental parallel support vector machines for classifying large-scale multi-class image datasets. In: Dang, T.K., Wagner, R., K\u00fcng, J., Thoai, N., Takizawa, M., Neuhold, E. (eds.) FDSE 2016. LNCS, vol. 10018, pp. 20\u201339. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-48057-2_2"},{"issue":"4","key":"20_CR13","doi-asserted-by":"publisher","first-page":"1199","DOI":"10.1007\/s11042-014-2049-4","volume":"74","author":"T-N Doan","year":"2014","unstructured":"Doan, T.-N., Do, T.-N., Poulet, F.: Large scale classifiers for visual classification tasks. Multimed. Tools Appl. 74(4), 1199\u20131224 (2014). https:\/\/doi.org\/10.1007\/s11042-014-2049-4","journal-title":"Multimed. Tools Appl."},{"issue":"4","key":"20_CR14","first-page":"1871","volume":"9","author":"RE Fan","year":"2008","unstructured":"Fan, R.E., Chang, K.W., Hsieh, C.J., Wang, X.R., Lin, C.J.: LIBLINEAR: a library for large linear classification. J. Mach. Learn. Res. 9(4), 1871\u20131874 (2008)","journal-title":"J. Mach. Learn. Res."},{"key":"20_CR15","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. CoRR abs\/1512.03385 (2015)","DOI":"10.1109\/CVPR.2016.90"},{"key":"20_CR16","unstructured":"Japkowicz, N. (ed.): AAAI Workshop on Learning from Imbalanced Data Sets. No. WS-00-05 in AAAI Tech report (2000)"},{"key":"20_CR17","first-page":"255","volume-title":"Advances in Kernel Methods","author":"UHG Kre\u00dfel","year":"1999","unstructured":"Kre\u00dfel, U.H.G.: Pairwise classification and support vector machines. In: Sch\u00f6lkopf, B., Burges, C.J.C., Smola, A.J. (eds.) Advances in Kernel Methods, pp. 255\u2013268. MIT Press, Cambridge (1999)"},{"key":"20_CR18","unstructured":"Li, F., Perona, P.: A Bayesian hierarchical model for learning natural scene categories. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), 20\u201326 June 2005, San Diego, CA, USA, pp. 524\u2013531 (2005)"},{"issue":"2","key":"20_CR19","doi-asserted-by":"publisher","first-page":"539","DOI":"10.1109\/TSMCB.2008.2007853","volume":"39","author":"XY Liu","year":"2009","unstructured":"Liu, X.Y., Wu, J., Zhou, Z.H.: Exploratory undersampling for class-imbalance learning. IEEE Trans. Syst. Man Cybern. Part B 39(2), 539\u2013550 (2009)","journal-title":"IEEE Trans. Syst. Man Cybern. Part B"},{"key":"20_CR20","doi-asserted-by":"crossref","unstructured":"Lowe, D.: Object recognition from local scale invariant features. In: Proceedings of the 7th International Conference on Computer Vision, pp. 1150\u20131157 (1999)","DOI":"10.1109\/ICCV.1999.790410"},{"key":"20_CR21","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","volume":"60","author":"D Lowe","year":"2004","unstructured":"Lowe, D.: Distinctive image features from scale invariant keypoints. Int. J. Comput. Vis. 60, 91\u2013110 (2004)","journal-title":"Int. J. Comput. Vis."},{"key":"20_CR22","unstructured":"OpenMP Architecture Review Board: OpenMP application program interface version 3.0 (2008). http:\/\/www.openmp.org\/mp-documents\/spec30.pdf"},{"key":"20_CR23","doi-asserted-by":"crossref","unstructured":"Perronnin, F., S\u00e1nchez, J., Liu, Y.: Large-scale image categorization with explicit data embedding. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2297\u20132304 (2010)","DOI":"10.1109\/CVPR.2010.5539914"},{"key":"20_CR24","doi-asserted-by":"crossref","unstructured":"Ricamato, M.T., Marrocco, C., Tortorella, F.: MCS-based balancing techniques for skewed classes: an empirical comparison. In: ICPR, pp.\u00a01\u20134 (2008)","DOI":"10.1109\/ICPR.2008.4761359"},{"key":"20_CR25","doi-asserted-by":"crossref","unstructured":"Shalev-Shwartz, S., Singer, Y., Srebro, N.: Pegasos: primal estimated sub-gradient solver for SVM. In: Proceedings of the Twenty-Fourth International Conference Machine Learning, pp. 807\u2013814. ACM (2007)","DOI":"10.1145\/1273496.1273598"},{"key":"20_CR26","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR abs\/1409.1556 (2014)"},{"key":"20_CR27","doi-asserted-by":"crossref","unstructured":"Sivic, J., Zisserman, A.: Video google: a text retrieval approach to object matching in videos. In: 9th IEEE International Conference on Computer Vision (ICCV 2003), 14\u201317 October 2003, Nice, France, pp. 1470\u20131477 (2003)","DOI":"10.1109\/ICCV.2003.1238663"},{"key":"20_CR28","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. CoRR abs\/1512.00567 (2015)","DOI":"10.1109\/CVPR.2016.308"},{"key":"20_CR29","doi-asserted-by":"publisher","unstructured":"Vapnik, V.: The Nature of Statistical Learning Theory. Springer, Heidelberg (1995). https:\/\/doi.org\/10.1007\/978-1-4757-3264-1","DOI":"10.1007\/978-1-4757-3264-1"},{"issue":"6","key":"20_CR30","doi-asserted-by":"publisher","first-page":"893","DOI":"10.1162\/neco.1993.5.6.893","volume":"5","author":"V Vapnik","year":"1993","unstructured":"Vapnik, V., Bottou, L.: Local algorithms for pattern recognition and dependencies estimation. Neural Comput. 5(6), 893\u2013909 (1993)","journal-title":"Neural Comput."},{"key":"20_CR31","unstructured":"Visa, S., Ralescu, A.: Issues in mining imbalanced data sets - a review paper. In: Midwest Artificial Intelligence and Cognitive Science Conference, Dayton, USA, pp. 67\u201373 (2005)"},{"key":"20_CR32","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1613\/jair.1199","volume":"19","author":"GM Weiss","year":"2003","unstructured":"Weiss, G.M., Provost, F.: Learning when training data are costly: the effect of class distribution on tree induction. J. Artif. Intell. Res. 19, 315\u2013354 (2003)","journal-title":"J. Artif. Intell. Res."},{"key":"20_CR33","doi-asserted-by":"crossref","unstructured":"Wu, J.: Power mean SVM for large scale visual classification. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2344\u20132351 (2012)","DOI":"10.1109\/CVPR.2012.6247946"}],"container-title":["Lecture Notes in Networks and Systems","Modelling, Computation and Optimization in Information Systems and Management Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-92666-3_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T22:46:26Z","timestamp":1726267586000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-92666-3_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,8]]},"ISBN":["9783030926656","9783030926663"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-92666-3_20","relation":{},"ISSN":["2367-3370","2367-3389"],"issn-type":[{"type":"print","value":"2367-3370"},{"type":"electronic","value":"2367-3389"}],"subject":[],"published":{"date-parts":[[2021,12,8]]},"assertion":[{"value":"8 December 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MCO","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Modelling, Computation and Optimization in Information Systems and Management Sciences","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hanoi","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vietnam","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 December 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 December 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mco2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/mco2021.event.univ-lorraine.fr\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}