Online Learning Task
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An Online Learning Task is a learning task that is an online/serial decisioning task (where soon after the prediction is made, the true label of the instance is discovered).
- Context:
- It can range from being a Supervised Online Learning Task to being an Unsupervised Online Learning Task.
- It can be solved by an Online Learning System that applies an (online learning algorithm).
- It can range from being a Heuristic Online Learning Task to being a Data-Driven Online Learning Task.
- It can range from being a Reinforcement Learning Task to being a ...
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- Example(s):
- Counter-Example(s):
- See: Web-based Learning Task, Active Learning, Adversarial Game, Self-Supervised Learning Task, Exploration/Exploitation Tradeoff, Incremental Learning, Credit Assignment Task.
References
2017
- (Auer, 2017) ⇒ Peter Auer. (2017). "Online Learning". In: (Sammut & Webb, 2017).
- QUOTE: Online learning and its variants are one of the main models of computational learning theory, complementing statistical PAC learning and related models. An online learner needs to make predictions about a sequence of instances, one after the other, and receives feedback after each prediction. The performance of the online learner is typically compared to the best predictor from a given class, often in terms of its excess loss (the regret) over the best predictor. Some of the fundamental online learning algorithms and their variants are discussed: weighted majority, follow the perturbed leader, follow the regularized leader, the perceptron algorithm, the doubling trick, bandit algorithms, and the issue of adaptive versus oblivious instance sequences. A typical performance proof of an online learning algorithm is exemplified for the perceptron algorithm.
2014
- (Wikipedia, 2014) ⇒ http://en.wikipedia.org/wiki/online_machine_learning Retrieved:2014-11-20.
- Online machine learning is a model of induction that learns one instance at a time. The goal in on-line learning is to predict labels for instances. For example, the instances could describe the current conditions of the stock market, and an online algorithm predicts tomorrow's value of a particular stock. The key defining characteristic of on-line learning is that soon after the prediction is made, the true label of the instance is discovered.
2010
- (Bartlett, 2010) ⇒ Peter L. Bartlett. (2010). http://videolectures.net/mlss2010au_bartlett_onlinelearning/
2007
- (Blum & Monsour, 2007) ⇒ Avrim Blum, and Yishay Monsour. (2007). “Learning, Regret Minimization, and Equilibria.” xx xxx.
- ABSTRACT: Many situations involve repeatedly making decisions in an uncertain environment: for instance, deciding what route to drive to work each day, or repeated play of a game against an opponent with an unknown strategy. ...