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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Book ChapterDOI
A Case-Based Technique for Tracking Concept Drift in Spam Filtering
TL;DR: A case-based system for spam filtering called ECUE is presented that can learn dynamically and the benefit of periodically redoing the feature selection process to bring new features into play is explored.
Journal ArticleDOI
The Mondrian Detection Algorithm for Sonar Imagery
TL;DR: A new algorithm called the Mondrian detector has been developed for object detection in high-frequency synthetic aperture sonar (SAS) imagery and the results show that—as with Mondrian’s art—simplicity can be powerful.
Journal ArticleDOI
Towards cost-sensitive adaptation
TL;DR: This study considers potential model update as an investment decision, which, as in the financial markets, should be taken only if a certain return on investment is expected and proposes a reference framework for analyzing adaptation strategies in terms of costs and benefits.
Journal ArticleDOI
Fuzzy classification in dynamic environments
TL;DR: The present paper presents an incremental fuzzy rule-based system for classification purposes and explains how fuzzy rules can be continuously online generated to meet the requirements of non-stationary dynamic environments, where data arrives over long periods of time.
Journal ArticleDOI
Very fast decision rules for classification in data streams
Petr Kosina,João Gama +1 more
TL;DR: The adaptive extension (AVFDR) to detect changes in the process generating data and adapt the decision model and the experimental evaluation demonstrates that algorithms achieve competitive results in comparison to alternative methods and the adaptive methods are able to learn fast and compact rule sets from evolving streams.
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.