Nội dung được dịch bởi AI, chỉ mang tính chất tham khảo
Học giao tiếp tăng cường: Thuật toán và ứng dụng trong nhận dạng mẫu
Tóm tắt
Các phương pháp tĩnh hiệu quả nhất trong học máy không cung cấp sự thay thế nào cho quá trình tiến hóa và thích ứng động để tích hợp dữ liệu mới hoặc tái cấu trúc các vấn đề đã được học một phần. Trong lĩnh vực này, học tăng cường đại diện cho một sự thay thế thú vị và là một lĩnh vực nghiên cứu mở, trở thành một trong những mối quan tâm chính của cộng đồng học máy và phân loại. Bài viết này nghiên cứu các kỹ thuật học tăng cường có giám sát và ứng dụng của chúng, đặc biệt trong lĩnh vực nhận dạng mẫu. Bài viết trình bày tổng quan về các khái niệm chính và thuật toán có giám sát của học tăng cường, bao gồm tổng hợp các nghiên cứu đã được thực hiện trong lĩnh vực này và tập trung vào mạng nơ-ron, cây quyết định và máy vector hỗ trợ.
Từ khóa
#học tăng cường #học máy #nhận dạng mẫu #thuật toán #mạng nơ-ron #cây quyết định #máy vector hỗ trợTài liệu tham khảo
Almaksour A (2011) Incremental learning of evolving fuzzy inference systems: application to handwritten gesture recognition. Ph.D. thesis, INSA de Rennes
Bai X, Ren P, Zhang H, Zhou J (2015) An incremental structured part model for object recognition. Neurocomputing 154:189–199
Cauwenberghs G, Poggio T (2001) Incremental and decremental support vector machine learning. In: Advances in neural information processing systems, pp 409–415
Chefrour A, Souici-Meslati L (2013) Un panorama de méthodes d’apprentissage incrémental. In: International conference on extraction and knowledge management, Toulouse, France, pp 57–70
Déniz O, Castrillón M, Lorenzo J, Hernández M (2002) An incremental learning algorithm for face recognition. International workshop on biometric authentication. Springer, New York, pp 1–9
Diehl CP, Cauwenberghs G (2003) SVM incremental learning adaptation and optimization. In: Proceedings of neural networks conference, vol 4, pp 2685–2690
Erdem Z, Polikar R, Gurgen F, Yumusak N (2005) Ensemble of SVMs for incremental learning. In: Proceedings of international workshop on multiple classifier systems. Springer, New York, pp 246–256
Fung G, Mangasarian OL (2002) Incremental support vector machine classification. In: Proceedings of the international conference on data mining (SIAM). Society for Industrial and Applied Mathematics, pp 247–260
Ghassabeh YA, Moghaddam HA (2007) A face recognition system using neural networks with incremental learning ability.In: Proceedings of the international symposium on computational intelligence in robotics and automation, pp 291–296
Gurney KR, Baker D, Rayner P, Denning S (2008) Interannual variations in continental-scale net carbon exchange and sensitivity to observing networks estimated from atmospheric CO2 inversions for the period 1980 to 2005. Global Biogeochem Cycles 22(3):1–17
Hacene G, Gripon V, Farrugia N, Arzel M, Jezequel M (2017) Incremental learning with pretrained convolutional neural networks and binary associative memories
Han S, Meng Z, Khan AS, Tong Y (2016) Incremental boosting convolutional neural network for facial action unit recognition. In: Advances in neural information processing systems, pp 109–117
Huang C, Ai H, Yamashita T, Lao S, Kawade M (2007) Incremental learning of boosted face detector. In: Proceedings of the 11th on computer vision IEEE, pp 1–8
Hulley G, Marwala T (2007) Evolving classifiers: Methods for incremental learning. arXiv preprint arXiv:0709.3965
Joshi P, Kulkarni P (2012) Incremental learning: areas and methods-a survey. Int J Data Min Knowl Manag Process 2(5):43
Kawewong A, Pimup R, Hasegawa O (2013) Incremental learning framework for indoor scene recognition. In: AAAI, pp 1–7
Lawal IA, Abdulkarim SA (2017) Adaptive svm for data stream classification, S Afr Comput J 29(1):27–42
Liu Y (2015) Incremental learning in deep neural networks. Master of Science Thesis, Tampere University of Technology
Loosli G (2010) Méthodes à noyaux pour la détection de contexte. Academic editions E
Lu Y, Boukharouba K, Boonært J, Fleury A, Lecoeuche S (2014) Application of an incremental svm algorithm for on-line human recognition from video surveillance using texture and color features. Neurocomputing 126:132–140
Luo J, Pronobis A, Caputo B, Jensfelt P (2007) Incremental learning for place recognition in dynamic environments. In: Proceedings of IEEE/RSJ international conference on intelligent robots and systems, pp 721–728
Mańdziuk J, Shastri L (2002) Incremental class learning approach and its application to handwritten digit recognition. Inf Sci 141(3–4):193–217.
Mohemmed A, Lu G, Kasabov N (2012) Evaluating span incremental learning for handwritten digit recognition. International conference on neural information processing. Springer, New York, pp 670–677
Molina JFG, Zheng L, Sertdemir M, Dinter DJ, Schönberg S, Rädle M (2014) Incremental learning with svm for multimodal classification of prostatic adenocarcinoma, PLoS One 9(4):e93600
Ozawa S, Pang S, Kasabov N (2008) Incremental learning of chunk data for online pattern classification systems. IEEE Trans Neural Netw 19(6):1061–1074
Ozawa S, Toh SL, Abe S, Pang S, Kasabov N (2005) Incremental learning of feature space and classifier for face recognition. Neural Netw 18(5–6):575–584
Polikar R, Upda L, Upda SS, Honavar V (2001) Learn++: an incremental learning algorithm for supervised neural networks. IEEE Trans Syst Man Cybern Part C 31(4):497–508
Prudent Y (2006) Système d’apprentissage incrémental et hybride. PhD thesis, INSA University, Rouen
Ralaivola L, dAlché Buc F (2001) Incremental support vector machine learning: A local approach. In: Proceedings of the international conference on artificial neural networks. Springer, New York, pp 322–330
Reddy KK, Liu J, Shah M (2009) Incremental action recognition using feature-tree. In: Proceedings of the 12th international conference on computer vision IEEE, pp 1010–1017
Ruping S (2001) Incremental learning with support vector machines. In: Proceedings of the international conference on Data Mining IEEE, pp 641–642
Salperwyck C, Lemaire V, de Bois DUDP (2010) Classification incrémentale supervisée: un panel introductif. In: AAFD, pp 121–148
Sarwar SS, Ankit A, Roy K (2017) Incremental learning in deep convolutional neural networks using partial network sharing. arXiv:1712.02719
Schlimmer JC, Fisher D (1986) A case study of incremental concept induction. AAAI 86, pp 496–501
Syed NA, Liu H, Sung KK (1999) Handling concept drifts in incremental learning with support vector machines. In: Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining, pp 317–321
Toh SL, Ozawa S (2003) A face recognition system using neural networks with incremental learning ability. In: Proceedings of the 8th Australian and New Zealand Conference on intelligent information systems, Citeseer, pp 389–394
Utgoff PE (1989) Incremental induction of decision trees. Mach Learn 4(2):161–186
Zhao H, Yuen PC, Kwok JT (2006) A novel incremental principal component analysis and its application for face recognition. IEEE Trans Syst Man Cybern Part B 36(4):873–886
Zou L, Zhang T, Cao Z (2009) An incremental learning algorithm based on Support Vector Machine for pattern recognition. In: Proceedings of the International Society for Optics and Photonics, vol 7496
Zribi M, Boujelbene Y (2016) The neural networks with an incremental learning algorithm approach for mass classification in breast cancer. Biomed Data Min 5(118):2