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Online machine learning information


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.

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Online machine learning

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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...

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Online learning

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Distance education Virtual school Online learning in higher education Massive open online courses Online machine learning, in computer science and statistics...

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Machine learning

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Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn...

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Quantum machine learning

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Quantum machine learning is the integration of quantum algorithms within machine learning programs. The most common use of the term refers to machine learning...

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Outline of machine learning

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outline is provided as an overview of and topical guide to machine learning: Machine learning – subfield of soft computing within computer science that...

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Adversarial machine learning

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Adversarial machine learning is the study of the attacks on machine learning algorithms, and of the defenses against such attacks. A survey from May 2020...

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Ensemble learning

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In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from...

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Automated machine learning

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Automated machine learning (AutoML) is the process of automating the tasks of applying machine learning to real-world problems. AutoML potentially includes...

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Educational technology

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encompasses several domains including learning theory, computer-based training, online learning, and m-learning where mobile technologies are used. The...

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Distance education

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(distributed learning, e-learning, m-learning, online learning, virtual classroom, etc.) are used roughly synonymously with distance education. E-learning has...

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International Conference on Machine Learning

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International Conference on Machine Learning (ICML) is the leading international academic conference in machine learning. Along with NeurIPS and ICLR...

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Supervised learning

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Supervised learning (SL) is a paradigm in machine learning where input objects (for example, a vector of predictor variables) and a desired output value...

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Journal of Machine Learning Research

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The Journal of Machine Learning Research is a peer-reviewed open access scientific journal covering machine learning. It was established in 2000 and the...

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Support vector machine

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In machine learning, support vector machines (SVMs, also support vector networks) are supervised max-margin models with associated learning algorithms...

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Deep learning

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Deep learning is the subset of machine learning methods based on neural networks with representation learning. The adjective "deep" refers to the use of...

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Stochastic gradient descent

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become an important optimization method in machine learning. Both statistical estimation and machine learning consider the problem of minimizing an objective...

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Reinforcement learning

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Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning. Reinforcement learning differs...

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Unsupervised learning

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Unsupervised learning is a method in machine learning where, in contrast to supervised learning, algorithms learn patterns exclusively from unlabeled data...

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Computational learning theory

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Theoretical results in machine learning mainly deal with a type of inductive learning called supervised learning. In supervised learning, an algorithm is given...

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Transfer learning

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Transfer learning (TL) is a technique in machine learning (ML) in which knowledge learned from a task is re-used in order to boost performance on a related...

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