Learning in the presence of concept drift and hidden contexts
Gerhard Widmer,Miroslav Kubat +1 more
TLDR
A family of learning algorithms that flexibly react to concept drift and can take advantage of situations where contexts reappear are described, including a heuristic that constantly monitors the system's behavior.Citations
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A survey on concept drift adaptation
TL;DR: The survey covers the different facets of concept drift in an integrated way to reflect on the existing scattered state of the art and aims at providing a comprehensive introduction to the concept drift adaptation for researchers, industry analysts, and practitioners.
Proceedings ArticleDOI
Mining time-changing data streams
TL;DR: An efficient algorithm for mining decision trees from continuously-changing data streams, based on the ultra-fast VFDT decision tree learner is proposed, called CVFDT, which stays current while making the most of old data by growing an alternative subtree whenever an old one becomes questionable, and replacing the old with the new when the new becomes more accurate.
Journal ArticleDOI
Transfer Learning for Reinforcement Learning Domains: A Survey
Matthew D. Taylor,Peter Stone +1 more
TL;DR: This article presents a framework that classifies transfer learning methods in terms of their capabilities and goals, and then uses it to survey the existing literature, as well as to suggest future directions for transfer learning work.
Proceedings ArticleDOI
Collaborative filtering with temporal dynamics
TL;DR: Two leading collaborative filtering recommendation approaches are revamp and a more sensitive approach is required, which can make better distinctions between transient effects and long term patterns.
Journal ArticleDOI
Data-driven Soft Sensors in the process industry
TL;DR: Characteristics of the process industry data which are critical for the development of data-driven Soft Sensors are discussed.
References
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Explanation-based generalization: a unifying view
TL;DR: This paper proposed a general, domain-independent mechanism, called EBG, that unifies previous approaches to explanation-based generalization, which is illustrated in the context of several example problems, and used to contrast several existing systems for explanation based generalization.
The ART of Adaptive Pattern Recognition by a Self-Organizing Neural Network. Revision,
TL;DR: In this article, the stability-plasticity dilemma and Adaptive Resonance Theory are discussed in the context of self-organizing learning and recognition systems, and the three R's: Recognition, Reinforcement, and Recall.
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A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features
Scott Cost,Steven L. Salzberg +1 more
TL;DR: A nearest neighbor algorithm for learning in domains with symbolic features, which produces excellent classification accuracy on three problems that have been studied by machine learning researchers: predicting protein secondary structure, identifying DNA promoter sequences, and pronouncing English text.
Journal ArticleDOI
Incremental Learning from Noisy Data
TL;DR: This paper first reviews a framework for discussing machine learning systems and then describes STAGGER in that framework, which is based on a distributed concept description which is composed of a set of weighted, symbolic characterizations.
Proceedings Article
Quantitative results concerning the utility of explanation-based learning
TL;DR: This paper summarizes a set of experiments measuring the effectiveness of PRODIGY's EBL method (and its components) in several different domains.