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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Any-time clustering of high frequency news streams
TL;DR: A large scale system for clustering a stream of news articles that was developed as part of the Geospace & Media Tool (GMT), and how the LSH based approximation achieves a large speedup at the cost of only few and small errors is demonstrated.
Journal ArticleDOI
AFCGD: an adaptive fuzzy classifier based on gradient descent
TL;DR: This paper introduces an efficient adaptive mechanism named adaptive fuzzy classifier based on gradient descent (AFCGD) for online learning of an evolving fuzzy model and derives online rule update formulas for modification of the classifier’s structure regarding the concept of data to minimize the misclassification error through gradient descent.
Proceedings ArticleDOI
Dynamically evolving fuzzy classifier for real-time classification of data streams
TL;DR: A novel evolving fuzzy rule-based classifier that can easily distinguish noise from new concepts and automatically handles noise, and is inherently adaptive and can attend to any minute changes as it learns the rules in online manner.
Journal ArticleDOI
A taxonomic look at instance-based stream classifiers
TL;DR: A refined taxonomy of instance selection and generation methods for the classification of data streams subject to concept drift is presented, which allows discrimination among a large number of methods which pre-existing taxonomies for offline instance selection methods did not distinguish.
Journal ArticleDOI
Prediction of members' return visit rates using a time factor
TL;DR: A novel and simple time function to increase/decrease the weight of the old data in evaluating various members' past behaviors is proposed in order to effectively distinguish the different kinds of member behaviors and predict return visit rates.
References
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A theory of the learnable
TL;DR: This paper regards learning as the phenomenon of knowledge acquisition in the absence of explicit programming, and gives a precise methodology for studying this phenomenon from a computational viewpoint.
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Instance-Based Learning Algorithms
TL;DR: This paper describes how storage requirements can be significantly reduced with, at most, minor sacrifices in learning rate and classification accuracy and extends the nearest neighbor algorithm, which has large storage requirements.
Book
Machine Learning: An Artificial Intelligence Approach
TL;DR: This book contains tutorial overviews and research papers on contemporary trends in the area of machine learning viewed from an AI perspective, including learning from examples, modeling human learning strategies, knowledge acquisition for expert systems, learning heuristics, discovery systems, and conceptual data analysis.
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Learnability and the Vapnik-Chervonenkis dimension
TL;DR: This paper shows that the essential condition for distribution-free learnability is finiteness of the Vapnik-Chervonenkis dimension, a simple combinatorial parameter of the class of concepts to be learned.
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Queries and Concept Learning
TL;DR: This work considers the problem of using queries to learn an unknown concept, and several types of queries are described and studied: membership, equivalence, subset, superset, disjointness, and exhaustiveness queries.