Online Learning Algorithm
An Online Learning Algorithm is a 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.
References
2013
- (Wikipedia, 2010) ⇒ http://en.wikipedia.org/wiki/Online_machine_learning
- In machine learning, on-line learning (or, more correctly, 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. This information can then be used to refine the prediction hypothesis used by the algorithm. The goal of the algorithm is to make predictions that are close to the true labels.
More formally, an online algorithm proceeds in a sequence of trials. Each trial can be decomposed into three steps. First the algorithm receives an instance. Second the algorithm predicts the label of the instance. Third the algorithm receives the true label of the instance.^{[1]} The third stage is the most crucial as the algorithm can use this label feedback to update its hypothesis for future trials. The goal of the algorithm is to minimize some performance criteria. For example, with stock market prediction the algorithm may attempt to minimize sum of the square distances between the predicted and true value of a stock. Another popular performance criterion is to minimize the number of mistakes when dealing with classification problems.
Because on-line learning algorithms continually receive label feedback, the algorithms are able to adapt and learn in difficult situations. Many online algorithms can give strong guarantees on performance even when the instances are not generated by a distribution. As long as a reasonably good classifier exists, the online algorithm will learn to predict correct labels. This good classifier must come from a previously determined set that depends on the algorithm. For example, two popular on-line algorithms perceptron and winnow can perform well when a hyperplane exists that splits the data into two categories. These algorithms can even be modified to do provably well even if the hyperplane is allowed to infrequently change during the on-line learning trials.
Unfortunately, the main difficulty of on-line learning is also a result of the requirement for continual label feedback. For many problems it is not possible to guarantee that accurate label feedback will be available in the near future. For example, when designing a system that learns how to do optical character recognition, typically some expert must label previous instances to help train the algorithm. In actual use of the OCR application, the expert is no longer available and no inexpensive outside source of accurate labels is available. Fortunately, there is a large class of problems where label feedback is always available. For any problem that consists of predicting the future, an on-line learning algorithm just needs to wait for the label to become available. This is true in our previous example of stock market prediction and many other problems.
- In machine learning, on-line learning (or, more correctly, 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. This information can then be used to refine the prediction hypothesis used by the algorithm. The goal of the algorithm is to make predictions that are close to the true labels.
- ↑ Littlestone, Nick; (1988) Learning Quickly When Irrelevant Attributes Abound: A New Linear-threshold Algorithm, Machine Learning 285-318(2), Kluwer Academic Publishers
2010
- (Bartlett, 2010) ⇒ Peter L. Bartlett. (2010). http://videolectures.net/mlss2010au_bartlett_onlinelearning/
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.