{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,25]],"date-time":"2026-01-25T01:44:12Z","timestamp":1769305452820,"version":"3.49.0"},"publisher-location":"New York, NY, USA","reference-count":29,"publisher":"ACM","license":[{"start":{"date-parts":[[2019,11,3]],"date-time":"2019-11-03T00:00:00Z","timestamp":1572739200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001381","name":"National Research Foundation Singapore","doi-asserted-by":"publisher","award":["AISG-RP-2018-004,A19C1a0018"],"award-info":[{"award-number":["AISG-RP-2018-004,A19C1a0018"]}],"id":[{"id":"10.13039\/501100001381","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2019,11,3]]},"DOI":"10.1145\/3357384.3357946","type":"proceedings-article","created":{"date-parts":[[2019,11,4]],"date-time":"2019-11-04T14:11:35Z","timestamp":1572876695000},"page":"1171-1180","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":37,"title":["Automatic Construction of Multi-layer Perceptron Network from Streaming Examples"],"prefix":"10.1145","author":[{"given":"Mahardhika","family":"Pratama","sequence":"first","affiliation":[{"name":"Nanyang Technological University, Singapore, Singapore"}]},{"given":"Choiru","family":"Za'in","sequence":"additional","affiliation":[{"name":"La Trobe University, Melbourne, Australia"}]},{"given":"Andri","family":"Ashfahani","sequence":"additional","affiliation":[{"name":"Nanyang Technological University, Singapore, Singapore"}]},{"given":"Yew Soon","family":"Ong","sequence":"additional","affiliation":[{"name":"Nanyang Technological University, Singapore, Singapore"}]},{"given":"Weiping","family":"Ding","sequence":"additional","affiliation":[{"name":"Nantong University, Nantong, China"}]}],"member":"320","published-online":{"date-parts":[[2019,11,3]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Autonomous Deep Learning: Continual Learning Approach for Dynamic Environments. In In SIAM International Conference on Data Mining.","author":"Ashfahani Andri","year":"2019","unstructured":"Andri Ashfahani and Mahardhika Pratama . 2019 . Autonomous Deep Learning: Continual Learning Approach for Dynamic Environments. In In SIAM International Conference on Data Mining. Andri Ashfahani and Mahardhika Pratama. 2019. Autonomous Deep Learning: Continual Learning Approach for Dynamic Environments. In In SIAM International Conference on Data Mining."},{"key":"e_1_3_2_1_2_1","volume-title":"Searching for exotic particles in high-energy physics with deep learning. Nature communications 5","author":"Baldi P.","year":"2014","unstructured":"P. Baldi , Paul D. Sadowski , and Daniel Whiteson . 2014. Searching for exotic particles in high-energy physics with deep learning. Nature communications 5 ( 2014 ), 4308. P. Baldi, Paul D. Sadowski, and Daniel Whiteson. 2014. Searching for exotic particles in high-energy physics with deep learning. Nature communications 5 (2014), 4308."},{"key":"e_1_3_2_1_3_1","volume-title":"Machine Learning Approaches for Improving Condition? Based Maintenance of Naval Propulsion Plants. Journal of Engineering for the Maritime Environment --, --","author":"Coraddu Andrea","year":"2014","unstructured":"Andrea Coraddu , Luca Oneto , Alessandro Ghio , Stefano Savio , Davide Anguita , and Massimo Figari . 2014. Machine Learning Approaches for Improving Condition? Based Maintenance of Naval Propulsion Plants. Journal of Engineering for the Maritime Environment --, -- ( 2014 ), --. Andrea Coraddu, Luca Oneto, Alessandro Ghio, Stefano Savio, Davide Anguita, and Massimo Figari. 2014. Machine Learning Approaches for Improving Condition? Based Maintenance of Naval Propulsion Plants. Journal of Engineering for the Maritime Environment --, -- (2014), --."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2012.136"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2011.2160459"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2014.2345382"},{"key":"e_1_3_2_1_7_1","volume-title":"Knowledge Discovery from Data Streams","author":"Gama Joao","year":"2006","unstructured":"Joao Gama . 2010. Knowledge Discovery from Data Streams ( 1 st ed.). Chapman & Hall\/CRC. 8] Jo\u00e3o Gama, Ricardo Fernandes, and Ricardo Rocha. 2006 . Decision Trees for Mining Data Streams. Intell. Data Anal. 10, 1 (Jan. 2006), 23--45. Joao Gama. 2010. Knowledge Discovery from Data Streams (1st ed.). Chapman & Hall\/CRC. 8] Jo\u00e3o Gama, Ricardo Fernandes, and Ricardo Rocha. 2006. Decision Trees for Mining Data Streams. Intell. Data Anal. 10, 1 (Jan. 2006), 23--45.","edition":"1"},{"key":"e_1_3_2_1_8_1","volume-title":"Article 44 (March","author":"Gama Jo\u00e3o","year":"2014","unstructured":"Jo\u00e3o Gama , Indre Zliobaite , Albert Bifet , Mykola Pechenizkiy , and Abdelhamid Bouchachia . 2014. A Survey on Concept Drift Adaptation. ACM Comput. Surv. 46, 4 , Article 44 (March 2014 ), 37 pages. Jo\u00e3o Gama, Indre Zliobaite, Albert Bifet, Mykola Pechenizkiy, and Abdelhamid Bouchachia. 2014. A Survey on Concept Drift Adaptation. ACM Comput. Surv. 46, 4, Article 44 (March 2014), 37 pages."},{"key":"e_1_3_2_1_9_1","unstructured":"Young Hun Jung Jack Goetz and Ambuj Tewari. [n. d.]. Online multiclass boosting. In Advances in Neural Information Processing Systems 30.  Young Hun Jung Jack Goetz and Ambuj Tewari. [n. d.]. Online multiclass boosting. In Advances in Neural Information Processing Systems 30."},{"key":"e_1_3_2_1_10_1","unstructured":"James Kirkpatrick Razvan Pascanu Neil Rabinowitz Joel Veness Guillaume Desjardins Andrei A. Rusu Kieran Milan John Quan Tiago Ramalho Agnieszka Grabska-Barwinska Demis Hassabis Claudia Clopath Dharshan Kumaran and Raia Hadsell. 2016. Overcoming catastrophic forgetting in neural networks. http:\/\/arxiv.org\/abs\/1612.00796 cite arxiv:1612.00796.  James Kirkpatrick Razvan Pascanu Neil Rabinowitz Joel Veness Guillaume Desjardins Andrei A. Rusu Kieran Milan John Quan Tiago Ramalho Agnieszka Grabska-Barwinska Demis Hassabis Claudia Clopath Dharshan Kumaran and Raia Hadsell. 2016. Overcoming catastrophic forgetting in neural networks. http:\/\/arxiv.org\/abs\/1612.00796 cite arxiv:1612.00796."},{"key":"e_1_3_2_1_11_1","unstructured":"David Lopez-Paz and Marc' Aurelio Ranzato. [n. d.]. Gradient Episodic Memory for Continual Learning. In Advances in Neural Information Processing Systems 30.  David Lopez-Paz and Marc' Aurelio Ranzato. [n. d.]. Gradient Episodic Memory for Continual Learning. In Advances in Neural Information Processing Systems 30."},{"key":"e_1_3_2_1_12_1","volume-title":"On the Number of Linear Regions of Deep Neural Networks. In Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014","author":"Mont\u00fafar Guido F.","year":"2014","unstructured":"Guido F. Mont\u00fafar , Razvan Pascanu , KyungHyun Cho , and Yoshua Bengio . 2014 . On the Number of Linear Regions of Deep Neural Networks. In Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014 , December 8 --13 2014, Montreal, Quebec, Canada. 2924--2932. http:\/\/papers.nips.cc\/paper\/ 5422-on-the-number-of-linear-regions-of-deep-neural-networks Guido F. Mont\u00fafar, Razvan Pascanu, KyungHyun Cho, and Yoshua Bengio. 2014. On the Number of Linear Regions of Deep Neural Networks. In Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, December 8--13 2014, Montreal, Quebec, Canada. 2924--2932. http:\/\/papers.nips.cc\/paper\/ 5422-on-the-number-of-linear-regions-of-deep-neural-networks"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/TFUZZ.2014.2322385"},{"key":"e_1_3_2_1_14_1","volume-title":"Machine Learning: A Probabilistic Perspective","author":"Murphy Kevin P.","year":"2012","unstructured":"Kevin P. Murphy . 2012 . Machine Learning: A Probabilistic Perspective . The MIT Press . Kevin P. Murphy. 2012. Machine Learning: A Probabilistic Perspective. The MIT Press."},{"key":"e_1_3_2_1_15_1","volume-title":"Eighth International Workshop on Artificial Intelligence and Statistics. 105--112","author":"Oza Nikunj C","year":"2001","unstructured":"C Oza Nikunj and January Russell Stuart . 2001 . Online bagging and boosting. Jaakkola Tommi and Richardson Thomas, editors . In Eighth International Workshop on Artificial Intelligence and Statistics. 105--112 . C Oza Nikunj and January Russell Stuart. 2001. Online bagging and boosting. Jaakkola Tommi and Richardson Thomas, editors. In Eighth International Workshop on Artificial Intelligence and Statistics. 105--112."},{"key":"e_1_3_2_1_16_1","volume-title":"Reservoir of diverse adaptive learners and stacking fast hoeffding drift detection methods for evolving data streams. Machine Learning (01","author":"Pesaranghader Ali","year":"2018","unstructured":"Ali Pesaranghader , Herna Viktor , and Eric Paquet . 2018. Reservoir of diverse adaptive learners and stacking fast hoeffding drift detection methods for evolving data streams. Machine Learning (01 Jun 2018 ). https:\/\/doi.org\/10.1007\/ s10994-018--5719-z Ali Pesaranghader, Herna Viktor, and Eric Paquet. 2018. Reservoir of diverse adaptive learners and stacking fast hoeffding drift detection methods for evolving data streams. Machine Learning (01 Jun 2018). https:\/\/doi.org\/10.1007\/ s10994-018--5719-z"},{"key":"e_1_3_2_1_17_1","unstructured":"M. Pratama W. Pedrycz and E. Lughofer. 2018. Evolving Ensemble Fuzzy Classifier. IEEE Transactions on Fuzzy Systems (2018) 1--1.  M. Pratama W. Pedrycz and E. Lughofer. 2018. Evolving Ensemble Fuzzy Classifier. IEEE Transactions on Fuzzy Systems (2018) 1--1."},{"key":"e_1_3_2_1_18_1","unstructured":"\"Andrei A. Rusu\"; \"Neil C. Rabinowitz\". 2016. Progressive Neural Networks. (2016).  \"Andrei A. Rusu\"; \"Neil C. Rabinowitz\". 2016. Progressive Neural Networks. (2016)."},{"key":"e_1_3_2_1_19_1","unstructured":"D. Sahoo Q. D. Pham J. Lu and S. C. Hoi. 2017. Online Deep Learning: Learning Deep Neural Networks on the Fly. arXiv preprint arXiv:1711.03705 abs\/1711.03705 (2017).  D. Sahoo Q. D. Pham J. Lu and S. C. Hoi. 2017. Online Deep Learning: Learning Deep Neural Networks on the Fly. arXiv preprint arXiv:1711.03705 abs\/1711.03705 (2017)."},{"key":"e_1_3_2_1_20_1","volume-title":"Prioritized Experience Replay. In International Conference on Learning Representations. Puerto Rico.","author":"Schaul Tom","year":"2016","unstructured":"Tom Schaul , John Quan , Ioannis Antonoglou , and David Silver . 2016 . Prioritized Experience Replay. In International Conference on Learning Representations. Puerto Rico. Tom Schaul, John Quan, Ioannis Antonoglou, and David Silver. 2016. Prioritized Experience Replay. In International Conference on Learning Representations. Puerto Rico."},{"key":"e_1_3_2_1_21_1","volume-title":"Proceedings of the 35th International Conference on Machine Learning. 4548--4557","author":"Serra Joan","year":"2018","unstructured":"Joan Serra , Didac Suris , Marius Miron , and Alexandros Karatzoglou . 2018 . Overcoming Catastrophic Forgetting with Hard Attention to the Task . In Proceedings of the 35th International Conference on Machine Learning. 4548--4557 . Joan Serra, Didac Suris, Marius Miron, and Alexandros Karatzoglou. 2018. Overcoming Catastrophic Forgetting with Hard Attention to the Task. In Proceedings of the 35th International Conference on Machine Learning. 4548--4557."},{"key":"e_1_3_2_1_22_1","unstructured":"K. Simonyan and A. Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014).  K. Simonyan and A. Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)."},{"key":"e_1_3_2_1_23_1","unstructured":"Rupesh K Srivastava Jonathan Masci Sohrob Kazerounian Faustino Gomez and J\u00fcrgen Schmidhuber. [n. d.]. Compete to Compute. In Advances in Neural Information Processing Systems 26.  Rupesh K Srivastava Jonathan Masci Sohrob Kazerounian Faustino Gomez and J\u00fcrgen Schmidhuber. [n. d.]. Compete to Compute. In Advances in Neural Information Processing Systems 26."},{"key":"e_1_3_2_1_24_1","volume-title":"Chan","author":"Stolfo Salvatore J.","year":"2000","unstructured":"Salvatore J. Stolfo , Wei Fan , Wenke Lee , Andreas Prodromidis , and Philip K . Chan . 2000 . Cost-based Modeling for Fraud and Intrusion Detection: Results from the JAM Project. In In Proceedings of the 2000 DARPA Information Survivability Conference and Exposition. IEEE Computer Press , 130--144. Salvatore J. Stolfo,Wei Fan,Wenke Lee, Andreas Prodromidis, and Philip K. Chan. 2000. Cost-based Modeling for Fraud and Intrusion Detection: Results from the JAM Project. In In Proceedings of the 2000 DARPA Information Survivability Conference and Exposition. IEEE Computer Press, 130--144."},{"key":"e_1_3_2_1_25_1","volume-title":"International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=BJlxm30cKm","author":"Toneva Mariya","unstructured":"Mariya Toneva , Alessandro Sordoni , Remi Tachet des Combes , Adam Trischler , Yoshua Bengio , and Geoffrey J. Gordon . 2019. An Empirical Study of Example Forgetting during Deep Neural Network Learning . In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=BJlxm30cKm Mariya Toneva, Alessandro Sordoni, Remi Tachet des Combes, Adam Trischler, Yoshua Bengio, and Geoffrey J. Gordon. 2019. An Empirical Study of Example Forgetting during Deep Neural Network Learning. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=BJlxm30cKm"},{"key":"e_1_3_2_1_26_1","first-page":"1","article-title":"The Power of Depth for Feed-forward Neural Networks","volume":"49","author":"Wolpert D. H.","year":"2016","unstructured":"D. H. Wolpert . 2016 . The Power of Depth for Feed-forward Neural Networks . Journal of Machine Learning Research 49 (2016), 1 -- 39 . D. H. Wolpert. 2016. The Power of Depth for Feed-forward Neural Networks. Journal of Machine Learning Research 49 (2016), 1--39.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.2.461"},{"key":"e_1_3_2_1_28_1","unstructured":"Jaehong Yoon Eunho Yang Jeongtae Lee and Sung Ju Hwang. 2018. Lifelong Learning with Dynamically Expandable Networks. ICLR.  Jaehong Yoon Eunho Yang Jeongtae Lee and Sung Ju Hwang. 2018. Lifelong Learning with Dynamically Expandable Networks. ICLR."},{"key":"e_1_3_2_1_29_1","first-page":"1453","article-title":"Online incremental feature learning with denoising autoencoders","volume":"22","author":"Zhou Guanyu","year":"2012","unstructured":"Guanyu Zhou , Kihyuk Sohn , and Honglak Lee . 2012 . Online incremental feature learning with denoising autoencoders . Journal of Machine Learning Research 22 (2012), 1453 -- 1461 . Guanyu Zhou, Kihyuk Sohn, and Honglak Lee. 2012. Online incremental feature learning with denoising autoencoders. Journal of Machine Learning Research 22 (2012), 1453--1461.","journal-title":"Journal of Machine Learning Research"}],"event":{"name":"CIKM '19: The 28th ACM International Conference on Information and Knowledge Management","location":"Beijing China","acronym":"CIKM '19","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 28th ACM International Conference on Information and Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3357384.3357946","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3357384.3357946","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T23:23:04Z","timestamp":1750202584000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3357384.3357946"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,11,3]]},"references-count":29,"alternative-id":["10.1145\/3357384.3357946","10.1145\/3357384"],"URL":"https:\/\/doi.org\/10.1145\/3357384.3357946","relation":{},"subject":[],"published":{"date-parts":[[2019,11,3]]},"assertion":[{"value":"2019-11-03","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}