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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Dissertation
Defining, Designing and Evaluating Social Navigation
TL;DR: The results partly indicate that social Navigation adds quality to a system, that social navigation works well with other navigational aids, and that recommender systems need not be bootstrapped.
Proceedings Article
Batchwise Patching of Classifiers
TL;DR: This work presents classifier patching, an approach for adapting an existing black-box classification model to new data that adapts quickly and achieves high classification accuracy, outperforming state-of-the-art competitors in either adaptation speed or accuracy in many scenarios.
Book ChapterDOI
An efficient algorithm for instance-based learning on data streams
Jürgen Beringer,Eyke Hüllermeier +1 more
TL;DR: This paper considers the problem of classification on data streams and develops an instance-based learning algorithm for that purpose and suggests that this algorithm has a number of desirable properties that are not, at least not as a whole, shared by currently existing alternatives.
Journal ArticleDOI
Classifying evolving data streams with partially labeled data
TL;DR: This paper proposes a new semi-supervised approach for handling concept-drifting data streams containing both labeled and unlabeled instances that is so general that it can be applied to different classification models.
Journal ArticleDOI
A Precise Statistical approach for concept change detection in unlabeled data streams
TL;DR: This paper presents a Precise Statistical approach for Concept Change Detection in unlabeled data streams, which, abbreviated as PSCCD, detects changes using an exchangeable test based on Doob’s Maximal Inequality.
References
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Proceedings ArticleDOI
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.
Journal ArticleDOI
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.
Journal ArticleDOI
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.
Journal ArticleDOI
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.