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A review of online supervised learning

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Abstract

Online learning emerged as a promising solution to handle large data problems. The high-level performance witnessed in real-life applications of online learning established dramatic advances in this field. The varying nature of the data needs special attention from a research point of view, as it has emerged as a common challenge in many domains. Interestingly, online learning response to this varying nature of the data is one of the promising solutions. We continue in this direction by covering successful algorithms in literature and their complexities to meet new challenges in this field. In particular, we have covered the working of online supervised learning algorithms and their bounds on mistake rate. A suitable and systematic review of online supervised learning algorithms is crucial for domain understanding and a step toward a solution to meet future challenges in this field. We have covered online supervised learning review with its common framework, algorithms description in ascending order of their development of applications in real-life use, and discussion on their theoretical analysis of algorithms. The present paper also includes an experimental comparison to understand advances in online learning algorithms responses to benchmarked datasets as well as future challenges in this field.

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References

  • Agarwal A, Hazan E, Kale S, Schapire RE (2006) Algorithms for portfolio management based on the Newton method. In: Proceedings of the 23rd international conference on machine learning, pp 9–16

  • Agarwal N, Hazan E, Singh K (2019) Logarithmic regret for online control. In: Advances in neural information processing systems 32: annual conference on neural information processing systems 2019, NeurIPS 2019, 8–14 December 2019, Vancouver, BC, Canada, pp 10175–10184

  • Akcoglu K, Drineas P, Kao M-Y (2002) Fast universalization of investment strategies with provably good relative returns. In: 29th international colloquium on automata, languages, and programming, ICALP 2002; conference date: 08-07-2002 through 13-07-2002, pp 888–900

  • Baeza-Yates RA, Ribeiro-Neto B (1999) Modern information retrieval. Addison-Wesley Longman Publishing Co., Inc, New York

    Google Scholar 

  • Beck A, Teboulle M (2003) Mirror descent and nonlinear projected subgradient methods for convex optimization. Oper Res Lett 31:167–175

    Article  MathSciNet  MATH  Google Scholar 

  • Borodin A, El-Yaniv R, Gogan V (2003) Can we learn to beat the best stock. J Artif Intell Res 21:579–594

    Article  MathSciNet  MATH  Google Scholar 

  • Burges C, Shaked T, Renshaw E, Lazier A, Deeds M, Hamilton N, Hullender G (2005) Learning to rank using gradient descent. In: Proceedings of ICML 2005, pp. 89–96

  • Campolucci P, Uncini A, Piazza F, Rao BD (1999) On-line learning algorithms for locally recurrent neural networks. IEEE Trans Neural Netw 10(2):253–271

    Article  Google Scholar 

  • Cesa-Bianchi N, Lugosi G (2006) Prediction, learning, and games. Cambridge University Press, Cambridge

    Book  MATH  Google Scholar 

  • Cesa-Bianchi N, Freund Y, Haussler D, Helmbold D, Schapire R, Warmuth M (1997) How to use expert advice. J ACM 44:427–485

    Article  MathSciNet  MATH  Google Scholar 

  • Cesa-Bianchi N, Conconi A, Gentile C (2005) A second-order perceptron algorithm. SIAM J Comput 34(3):640–668

    Article  MathSciNet  MATH  Google Scholar 

  • Cossock D, Zhang T (2006) Subset ranking using regression COLT lecture notes in computer science, vol 4005. Springer, Berlin, pp 605–619

    MATH  Google Scholar 

  • Cover TM (1991) Universal portfolios. Math Financ 1(1):1–29

    Article  MathSciNet  MATH  Google Scholar 

  • Crammer K, Lee DD (2010) Learning via gaussian herding. Adv Neural Inf Process Syst 23:451–459

    Google Scholar 

  • Crammer K, Singer Y (2003) Ultraconservative online algorithms for multiclass problems. J Mach Learn Res 3:951–991

    MATH  Google Scholar 

  • Crammer K, Dekel O, Keshet J, Shalev-Shwartz S, Singer Y (2006) Online passive-aggressive algorithms. J Mach Learn Res 7:551–585

    MathSciNet  MATH  Google Scholar 

  • Crammer K, Kulesza A, Dredze M (2009) Adaptive regularization of weight vectors. In: Proceedings of the 22nd international conference on neural information processing systems, pp 414–422

  • Crammer K, Singer Y (2001) Pranking with ranking. In: Advances in neural information processing systems, vol 14. MIT Press, pp 641–647

  • Das AS, Datar M, Garg A, Rajaram S (2007) Google news personalization: Scalable online collaborative filtering. In: Proceedings of the 16th international conference on World Wide Web, pp 271–280

  • Das P, Banerjee A (2011) Meta optimization and its application to portfolio selection. In: Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 1163–1171

  • Dimitris F, Georgios P, Stratis S (2021) Efficient online learning for dynamic k-clustering. In: Proceedings of the 38th international conference on machine learning, vol 139, pp 3396–3406

  • Dredze M, Crammer K, Pereira F (2008) Confidence-weighted linear classification. In: Proceedings of the 25th international conference on machine learning, pp 264–271

  • Duchi J, Hazan E, Singer Y (2011) Adaptive subgradient methods for online learning and stochastic optimization. J Mach Learn Res 12:2121–2159

    MathSciNet  MATH  Google Scholar 

  • Gaivoronski AA, Stella F (2000) Stochastic nonstationary optimization for finding universal portfolios. Ann OR 100(1–4):165–188

    Article  MathSciNet  MATH  Google Scholar 

  • Gentile C (2002) A new approximate maximal margin classification algorithm. J Mach Learn Res 2:213–242

    MathSciNet  MATH  Google Scholar 

  • Goldberg K, Roeder T, Gupta D, Perkins C (2001) Eigentaste: a constant time collaborative filtering algorithm. Inf Retriev 4(2):133–151

    Article  MATH  Google Scholar 

  • Gonzalez A, Dorronsoro JR (2008) Natural conjugate gradient training of multilayer perceptrons. Neurocomputing 71(13–15):2499–2506

    Article  Google Scholar 

  • Gyorfi L, Schafer D (2003) Nonparametric prediction. In: Advances in learning theory: methods. IOS Press, NATO Science Series, Models and Applications, pp 341–356

  • Gyorfi L, Lugosi G, Udina F (2006) Nonparametric kernel-based sequential investment strategies. Int J Math Stat Financ Econ 16(2):337–357

    MathSciNet  MATH  Google Scholar 

  • Gyorfi L, Udina F, Walk H (2008) Nonparametric nearest neighbor based empirical portfolio selection strategies. Stat Risk Model 26:145–157

    MathSciNet  MATH  Google Scholar 

  • Hao S, Hu P, Zhao P, Hoi SCH, Miao C (2018) Online active learning with expert advice. ACM Trans Knowl Disc Data 12(5):1–22

    Article  Google Scholar 

  • Harrington EF (2003) Online ranking/collaborative filtering using the perceptron algorithm. In: Proceedings of the twentieth international conference on international conference on machine learning, ICML 03. AAAI Press, pp 250–257

  • Hazan E, Agarwal A, Kale S (2007) Logarithmic regret algorithms for online convex optimization. Mach Learn 69:169–192

    Article  MATH  Google Scholar 

  • Hazan E, Seshadhri C (2009) Efficient learning algorithms for changing environments. In: Proceedings of the 26th annual international conference on machine learning, pp 393–400

  • Heckel R, Ramchandran K (2017) The sample complexity of online one-class collaborative filtering. In: Proceedings of the 34th international conference on machine learning, vol 70, pp 1452–1460

  • Helmbold DP, Schapire RE, Singer Y, Warmuth MK (1998) On-line portfolio selection using multiplicative updates. Math Financ 8(4):325–347

    Article  MATH  Google Scholar 

  • Herbrich R, Graepel T, Obermayer K (1999) Support vector learning for ordinal regression

  • Hoi SC, Wang J, Zhao P (2014) Libol: a library for online learning algorithms. J Mach Learn Res 15(15):495–499

    MATH  Google Scholar 

  • Huang D, Zhou J, Li B, Hoi SCH, Zhou S (2016) Robust median reversion strategy for online portfolio selection. IEEE Trans Knowl Data Eng 28(9):2480–2493

    Article  Google Scholar 

  • Kelly JL (1956) A new interpretation of information rate. IRE Trans Inf Theory 2:185–189

    Article  Google Scholar 

  • Kivinen J, Warmuth MK (1997) Exponentiated gradient versus gradient descent for linear predictors. Inf Comput 132(1):1–63

    Article  MathSciNet  MATH  Google Scholar 

  • Koren Y, Bell R, Volinsky C (2009) Matrix factorization techniques for recommender systems. Computer 42(8):30–37

    Article  Google Scholar 

  • Li B, Hoi SCH (2014) Online portfolio selection: a survey. ACM Comput Surv 46(3):1–36

    MATH  Google Scholar 

  • Li Y, Long PM (2002) The relaxed online maximum margin algorithm. Mach Learn 46(1–3):361–387

    Article  MATH  Google Scholar 

  • Li B, Hoi SC, Gopalkrishnan V (2011) Corn: correlation-driven nonparametric learning approach for portfolio selection. ACM Trans Intell Syst Technol 2(3):1–25

    Article  Google Scholar 

  • Li B, Zhao P, Hoi S, Gopalkrishnan V (2012) Pamr: passive aggressive mean reversion strategy for portfolio selection. Mach Learn 87:221–258

    Article  MathSciNet  MATH  Google Scholar 

  • Li B, Hoi SCH, Zhao P, Gopalkrishnan V (2013) Confidence weighted mean reversion strategy for online portfolio selection. ACM Trans Knowl Discov Data 7(1):1–38

    Article  Google Scholar 

  • Li B, Hoi SC, Sahoo D, Liu Z-Y (2015a) Moving average reversion strategy for on-line portfolio selection. Artif Intell 222:104–123

    Article  MathSciNet  Google Scholar 

  • Li YX, Li ZJ, Wang F, Kuang L (2015b). Accelerated online learning for collaborative filtering and recommender systems. In: IEEE international conference on data mining workshops. ICDMW, pp 79-885. https://doi.org/10.1109/ICDMW.2014.95

  • Li B, Sahoo D, Hoi SCH (2016) Olps: a toolbox for on-line portfolio selection. J Mach Learn Res 17(1):1242–1246

    MathSciNet  Google Scholar 

  • Littlestone N (1988) Learning quickly when irrelevant attributes abound: a new linear-threshold algorithm. Mach Learn 2:285–318

    Article  Google Scholar 

  • Littlestone N, Warmuth MK (1994) The weighted majority algorithm. Inf Comput 108(2):212–261

    Article  MathSciNet  MATH  Google Scholar 

  • Liu C, Hoi SCH, Zhao P, Sun J (2016) Online arima algorithms for time series prediction. In: Proceedings of the thirtieth AAAI conference on artificial intelligence, pp 1867–1873

  • Lu J, Hoi S, Wang J, Zhao P (2013) Second order online collaborative filtering. J Mach Learn Res 29:325–340

    Google Scholar 

  • Luo L, Chen C, Zhang Z, Li W-J, Zhang T (2019) Robust frequent directions with application in online learning. J Mach Learn Res 20(45):1–41

    MathSciNet  MATH  Google Scholar 

  • Luo H, Agarwal A, Cesa-Bianchi N, Langford J (2016) Efficient second order online learning by sketching. In: Proceedings of the 30th international conference on neural information processing systems, pp 910–918

  • Novikoff A (1962) On convergence proofs on perceptrons. In: Proceedings of the symposium on the mathematical theory of automata, vol 12, pp 615–622

  • Orabona Francesco, Crammer Koby (2010) New adaptive algorithms for online classification. In: Advances in neural information processing systems, pp 1840–1848

  • Rosenblatt F (1958) The perceptron: a probabilistic model for information storage and organization the brain. Psychol Rev 65:386

    Article  Google Scholar 

  • Sahoo D, Pham Q, Lu J, Hoi SCH (2022) Online deep learning: learning deep neural networks on the fly. In: Proceedings of the twenty-seventh international joint conference on artificial intelligence, pp 2660–2666

  • Sanz J, Perera R, Huerta C (2012) Gear dynamics monitoring using discrete wavelet transformation and multi-layer perceptron neural networks. Appl Soft Comput 12(9):2867–2878

    Article  Google Scholar 

  • Sarwar B, Karypis G, Konstan J, Riedl J (2001) Item-based collaborative filtering recommendation algorithms. Proceedings of the 10th international conference on World Wide Web, pp 285–295

  • Sharpe W (1970) Portfolio theory and capital markets. Foundations of American Government and Political Science Series, McGraw Hill, New York

    Google Scholar 

  • Shi T, Zhu J (2017) Online bayesian passive-aggressive learning. J Mach Learn Res 18(33):1–39

    MathSciNet  MATH  Google Scholar 

  • Singh C, Sharma A (2020) Modified online Newton step based on elementwise multiplication. Comput Intell 36(3):1010–1025

    Article  MathSciNet  Google Scholar 

  • Singh C, Sharma A (2022) Online Newton step based on pseudo inverse and elementwise multiplication. In: Presented in 7th international conference on harmony search, soft computing and applications (ICHSA 2022) 23–24 Feb 2022, Seoul, South Korea

  • Su X, Khoshgoftaar TM (2009) A survey of collaborative filtering techniques. In: Advanced in artificial intelligence

  • Takapoui R (2022) Collaborative filtering via online mirror descent. Stanford University, Stanford

    Google Scholar 

  • van Erven T, Koolen WM (2016) Metagrad: multiple learning rates in online learning. Adv Neural Inf Process Syst 29:3666–3674

    MATH  Google Scholar 

  • Wan S, Banta LE (2006) Parameter incremental learning algorithm for neural networks. IEEE Trans Neural Netw 17(6):1424–1438

    Article  Google Scholar 

  • Wang J, Zhao P, Hoi SCH (2017) Soft confidence-weighted learning. ACM Trans Intell Syst Technol 8:1–32

    Google Scholar 

  • Wang J, Hoi SC, Zhao P, Liu Z-Y (2013) Online multi-task collaborative filtering for on-the-fly recommender systems. In: Proceedings of the 7th ACM conference on Recommender systems, pp 237–244

  • Wang J, Wan J, Zhang Y, Hoi S (2015) Solar: scalable online learning algorithms for ranking. In: Proceedings of the 53rd annual meeting of the association for computational linguistics and the 7th international joint conference on natural language processing (volume 1: long papers). Association for Computational Linguistics, Beijing, China, pp 1692–1701

  • Williams RJ, Zipser D (1989) A learning algorithm for continually running fully recurrent neural networks. Neural Comput 1(2):270–280

    Article  Google Scholar 

  • Yang L, Jin R, Ye J (2009) Online learning by ellipsoid method. In: Proceedings of the 26th annual international conference on machine learning, pp 1153–1160

  • Zinkevich M (2003) Online convex programming and generalized infinitesimal gradient ascent. In: Proceedings of the twentieth international conference on international conference on machine learning, pp 928–936

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Authors and Affiliations

  1. Department of Computer Science and Applications, Panjab University, Chandigarh, India

    Charanjeet Singh & Anuj Sharma

  2. Department of Mathematics, Panjab University, Chandigarh, India

    Charanjeet Singh

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  1. Charanjeet Singh
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  2. Anuj Sharma
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Correspondence to Anuj Sharma.

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Singh, C., Sharma, A. A review of online supervised learning. Evolving Systems 14, 343–364 (2023). https://doi.org/10.1007/s12530-022-09448-y

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