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Kumar, \u201cMachine learning from theory to algorithms: an overview,\u201d Journal of Physics: Conference Series, vol.1142, p.012012, IOP Publishing, 2018. 10.1088\/1742-6596\/1142\/1\/012012","DOI":"10.1088\/1742-6596\/1142\/1\/012012"},{"key":"5","doi-asserted-by":"publisher","unstructured":"[5] A.L.I. Oliveira, \u201cEstimation of software project effort with support vector regression,\u201d Neurocomputing, vol.69, no.13-15, pp.1749-1753, 2006. 10.1016\/j.neucom.2005.12.119","DOI":"10.1016\/j.neucom.2005.12.119"},{"key":"6","unstructured":"[6] I..I. of Software Engineering improvement and quality Management, \u201cAbout us, year = 2007, url = http:\/\/www.isem.co.th\/home\/index.php\/about-us, urldate = 2007.\u201d"},{"key":"7","doi-asserted-by":"publisher","unstructured":"[7] M. Fern\u00e1ndez-Diego and F. Gonz\u00e1lez-Ladr\u00f3n-De-Guevara, \u201cPotential and limitations of the isbsg dataset in enhancing software engineering research: A mapping review,\u201d Information and Software Technology, vol.56, no.6, pp.527-544, 2014. 10.1016\/j.infsof.2014.01.003","DOI":"10.1016\/j.infsof.2014.01.003"},{"key":"8","doi-asserted-by":"crossref","unstructured":"[8] J. Pennington, R. Socher, and C. Manning, \u201cGlove: Global vectors for word representation,\u201d Proc. 2014 conference on empirical methods in natural language processing (EMNLP), pp.1532-1543, 2014. 10.3115\/v1\/d14-1162","DOI":"10.3115\/v1\/D14-1162"},{"key":"9","unstructured":"[9] D. Tang, B. Qin, X. Feng, and T. Liu, \u201cEffective LSTMs for target-dependent sentiment classification,\u201d arXiv preprint arXiv:1512.01100, 2015."},{"key":"10","unstructured":"[10] X. Chen, L. Xu, Z. Liu, M. Sun, and H. Luan, \u201cJoint learning of character and word embeddings,\u201d 24th International Joint Conference on Artificial Intelligence, 2015."},{"key":"11","doi-asserted-by":"publisher","unstructured":"[11] G. Cybenko, \u201cApproximation by superpositions of a sigmoidal function,\u201d Mathematics of control, signals and systems, vol.2, no.4, pp.303-314, 1989. 10.1007\/bf02551274","DOI":"10.1007\/BF02551274"},{"key":"12","unstructured":"[12] D. Basak, S. Pal, and D.C. Patranabis, \u201cSupport vector regression,\u201d Neural Information Processing-Letters and Reviews, vol.11, no.10, pp.203-224, 2007."},{"key":"13","doi-asserted-by":"publisher","unstructured":"[13] C.-H. Wu, J.-M. Ho, and D.T. Lee, \u201cTravel-time prediction with support vector regression,\u201d IEEE Trans. Intell. Transp. Syst., vol.5, no.4, pp.276-281, 2004. 10.1109\/tits.2004.837813","DOI":"10.1109\/TITS.2004.837813"},{"key":"14","doi-asserted-by":"crossref","unstructured":"[14] P. Pan, Z. Xu, Y. Yang, F. Wu, and Y. Zhuang, \u201cHierarchical recurrent neural encoder for video representation with application to captioning,\u201d Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp.1029-1038, 2016. 10.1109\/cvpr.2016.117","DOI":"10.1109\/CVPR.2016.117"},{"key":"15","doi-asserted-by":"crossref","unstructured":"[15] J. Wang, L.C. Yu, K.R. Lai, and X. Zhang, \u201cDimensional sentiment analysis using a regional cnn-lstm model,\u201d Proc. 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp.225-230, 2016. 10.18653\/v1\/p16-2037","DOI":"10.18653\/v1\/P16-2037"},{"key":"16","doi-asserted-by":"publisher","unstructured":"[16] L.H. Son, A. Kumar, S.R. Sangwan, A. Arora, A. Nayyar, and M. 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