Online Machine Learning (ML) Algorithm
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An Online Machine Learning (ML) Algorithm is a machine learning algorithm that can be implemented into an online learning system (to solve an online learning task).
- Context:
- It can range from being an Online IID Learning Algorithm to being an Online Structured Learning Algorithm.
- Example(s):
- Counter-Example(s):
- See: Winnow Algorithm, Online Processing, Data Stream, Evolutionary Learning Algorithm, Real-Time Recurrent Learning (RTRL) Algorithm.
References
2020
- (Wikipedia, 2020) ⇒ https://en.wikipedia.org/wiki/Online_machine_learning Retrieved:2020-9-28.
- In computer science, online machine learning is a method of machine learning in which data becomes available in a sequential order and is used to update the best predictor for future data at each step, as opposed to batch learning techniques which generate the best predictor by learning on the entire training data set at once. Online learning is a common technique used in areas of machine learning where it is computationally infeasible to train over the entire dataset, requiring the need of out-of-core algorithms. It is also used in situations where it is necessary for the algorithm to dynamically adapt to new patterns in the data, or when the data itself is generated as a function of time, e.g., stock price prediction.
Online learning algorithms may be prone to catastrophic interference, a problem that can be addressed by incremental learning approaches.
- In computer science, online machine learning is a method of machine learning in which data becomes available in a sequential order and is used to update the best predictor for future data at each step, as opposed to batch learning techniques which generate the best predictor by learning on the entire training data set at once. Online learning is a common technique used in areas of machine learning where it is computationally infeasible to train over the entire dataset, requiring the need of out-of-core algorithms. It is also used in situations where it is necessary for the algorithm to dynamically adapt to new patterns in the data, or when the data itself is generated as a function of time, e.g., stock price prediction.
2018
- (Hoi et al., 2018) ⇒ Steven C. H. Hoi, Doyen Sahoo, Jing Lu, and Peilin Zhao. (2018). “Online Learning: A Comprehensive Survey.” arXiv preprint arXiv:1802.02871
- ABSTRACT: Online learning represents an important family of machine learning algorithms, in which a learner attempts to resolve an online prediction (or any type of decision-making) task by learning a model/hypothesis from a sequence of data instances one at a time. The goal of online learning is to ensure that the online learner would make a sequence of accurate predictions (or correct decisions) given the knowledge of correct answers to previous prediction or learning tasks and possibly additional information. This is in contrast to many traditional batch learning or offline machine learning algorithms that are often designed to train a model in batch from a given collection of training data instances. This survey aims to provide a comprehensive survey of the online machine learning literatures through a systematic review of basic ideas and key principles and a proper categorization of different algorithms and techniques. Generally speaking, according to the learning type and the forms of feedback information, the existing online learning works can be classified into three major categories: (i) supervised online learning where full feedback information is always available, (ii) online learning with limited feedback, and (iii) unsupervised online learning where there is no feedback available. Due to space limitation, the survey will be mainly focused on the first category, but also briefly cover some basics of the other two categories. Finally, we also discuss some open issues and attempt to shed light on potential future research directions in this field.
2008
- (Yang et al., 2008) ⇒ Xiaofeng Yang, Jian Su, Jun Lang, Chew Lim Tan, Ting Liu, and Sheng Li. (2008). “An Entity-Mention Model for Coreference Resolution with Inductive Logic Programming.” In: Proceedings of ACL Conference (ACL 2008).
- Culotta et al. (2007) present a system which uses an online learning approach to train a classifier to judge whether two entities are coreferential or not.
2006
- (Cesa-Bianchi & Lugosi, 2006) ⇒ Nicolo Cesa-Bianchi, and Gabor Lugosi. (2006). “Prediction, Learning, and Games.” In: Cambridge University Press. ISBN: 0521841089.