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IEEE Computer Society, CVPR, pp 1\u20139","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"722_CR137","doi-asserted-by":"crossref","unstructured":"Tai KS, Socher R, Manning CD (2015) Improved semantic representations from tree-structured long short-term memory networks. In: Proceedings of the 53rd annual meeting of the association for computational linguistics and the 7th international joint conference on natural language processing of the Asian federation of natural language processing, ACL, The Association for Computer Linguistics, pp 1556\u20131566","DOI":"10.3115\/v1\/P15-1150"},{"key":"722_CR138","doi-asserted-by":"crossref","unstructured":"Taigman Y, Yang M, Ranzato M, Wolf L (2014) Deepface: Closing the gap to human-level performance in face verification. In: IEEE conference on computer vision and pattern recognition. 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Knowledge-Based Systems 139:13\u201322","journal-title":"Knowledge-Based Systems"},{"key":"722_CR146","unstructured":"van\u00a0der Wilk M, Rasmussen CE, Hensman J (2017) Convolutional gaussian processes. In: Advances in neural information processing systems, NeurIPS, pp 2849\u20132858"},{"key":"722_CR147","doi-asserted-by":"crossref","unstructured":"Vincent P, Larochelle H, Bengio Y, Manzagol P (2008) Extracting and composing robust features with denoising autoencoders. In: Proceedings of the international conference of machine learning, ICML, vol 307, pp 1096\u20131103","DOI":"10.1145\/1390156.1390294"},{"key":"722_CR148","unstructured":"Vinyals O, Jia Y, Deng L, Darrell T (2012) Learning with recursive perceptual representations. In: Advances in neural information processing systems, NeurIPS, pp 2834\u20132842"},{"key":"722_CR149","unstructured":"Wang SI, Manning CD (2012) Baselines and bigrams: simple, good sentiment and topic classification. In: Proceedings of the 50th annual meeting of the association for computational linguistics (Volume 2: Short Papers), pp 90\u201394"},{"key":"722_CR150","unstructured":"Wang SI, Manning CD (2013) Fast dropout training. In: Proceedings of the international conference on machine learning, ICML, vol 28, pp 118\u2013126"},{"issue":"7","key":"722_CR151","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1109\/TSMC.2017.2759090","volume":"49","author":"G Wang","year":"2019","unstructured":"Wang G, Zhang G, Choi K, Lu J (2019a) Deep additive least squares support vector machines for classification with model transfer. IEEE Trans Syst Man Cybern Syst 49(7):1527\u20131540","journal-title":"IEEE Trans Syst Man Cybern Syst"},{"key":"722_CR152","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.patrec.2018.02.010","volume":"119","author":"J Wang","year":"2019","unstructured":"Wang J, Chen Y, Hao S, Peng X, Hu L (2019b) Deep learning for sensor-based activity recognition: A survey. Pattern Recogn Lett 119:3\u201311","journal-title":"Pattern Recogn Lett"},{"key":"722_CR153","doi-asserted-by":"crossref","unstructured":"Widrow B, Hoff ME (1960) Adaptive switching circuits. Stanford Univ CA Stanford Electronics Labs, Tech. rep","DOI":"10.21236\/AD0241531"},{"key":"722_CR154","unstructured":"Wiering MA, Schomaker LR (2014) Multi-layer support vector machines. Regularization, optimization, kernels, and support vector machines p 457"},{"issue":"2","key":"722_CR155","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/S0893-6080(05)80023-1","volume":"5","author":"DH Wolpert","year":"1992","unstructured":"Wolpert DH (1992) Stacked generalization. Neural Netw 5(2):241\u2013259","journal-title":"Neural Netw"},{"issue":"1","key":"722_CR156","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1109\/4235.585893","volume":"1","author":"DH Wolpert","year":"1997","unstructured":"Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67\u201382","journal-title":"IEEE Trans Evol Comput"},{"key":"722_CR157","unstructured":"Wong SC, Gatt A, Stamatescu V, McDonnell MD (2016) Understanding data augmentation for classification: When to warp? In: International conference on digital image computing: techniques and applications. DICTA, IEEE, pp 1\u20136"},{"key":"722_CR158","unstructured":"Wu Y, Schuster M, Chen Z, Le QV, Norouzi M, Macherey W, Krikun M, Cao Y, Gao Q, Macherey K, Klingner J, Shah A, Johnson M, Liu X, Kaiser L, Gouws S, Kato Y, Kudo T, Kazawa H, Stevens K, Kurian G, Patil N, Wang W, Young C, Smith J, Riesa J, Rudnick A, Vinyals O, Corrado G, Hughes M, Dean J (2016) Google\u2019s neural machine translation system: Bridging the gap between human and machine translation. arXiv preprint: arXiv:1609.08144"},{"key":"722_CR159","unstructured":"Xu R (2013) Improvements to random forest methodology. PhD thesis, Iowa State University"},{"key":"722_CR160","unstructured":"Yang H, Wu J (2012) Practical large scale classification with additive kernels. 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In: Proceedings of AAAI conference on artificial intelligence, pp 2838\u20132844","DOI":"10.1609\/aaai.v31i1.10769"},{"key":"722_CR164","doi-asserted-by":"crossref","first-page":"21954","DOI":"10.1109\/ACCESS.2017.2762418","volume":"5","author":"C Yin","year":"2017","unstructured":"Yin C, Zhu Y, Fei J, He X (2017) A deep learning approach for intrusion detection using recurrent neural networks. IEEE Access 5:21954\u201321961","journal-title":"IEEE Access"},{"key":"722_CR165","unstructured":"Yu D, Deng L (2011) Deep convex net: a scalable architecture for speech pattern classification. In: Annual conference of the international speech communication association, INTERSPEECH, pp 2285\u20132288"},{"key":"722_CR166","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1016\/j.patrec.2017.09.018","volume":"115","author":"M Zareapoor","year":"2018","unstructured":"Zareapoor M, Shamsolmoali P, Jain DK, Wang H, Yang J (2018) Kernelized support vector machine with deep learning: an efficient approach for extreme multiclass dataset. Pattern Recogn Lett 115:4\u201313","journal-title":"Pattern Recogn Lett"},{"issue":"1","key":"722_CR167","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1093\/bioinformatics\/bti736","volume":"22","author":"HH Zhang","year":"2006","unstructured":"Zhang HH, Ahn J, Lin X, Park C (2006) Gene selection using support vector machines with non-convex penalty. 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