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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Journal ArticleDOI
An online ensembles approach for handling concept drift in data streams: diversified online ensembles detection
Parneeta Sidhu,Mps Bhatia +1 more
TL;DR: This work presents a novel online ensemble approach, Diversified online ensembles detection (DOED), for handling drifting concepts in data streams and proves it to be highly resource effective achieving very high accuracies even in a resource constrained environment.
Journal ArticleDOI
An efficient incremental learning mechanism for tracking concept drift in spam filtering.
TL;DR: This research focuses on the analysis of email’s header and applies decision tree data mining technique to look for the association rules about spams and proposes an efficient systematic filtering method based on these association rules.
Journal ArticleDOI
AMANDA: Semi-supervised density-based adaptive model for non-stationary data with extreme verification latency
TL;DR: AMANDA, a semi-supervised density-based adaptive model for non-stationary data, has two variations: AMANDA-FCP, which selects a fixed number of samples; and AMANda-DCP, which, in turn, dynamically selects samples from data.
Dissertation
Les approches chaos-stochastiques du risque de marché
TL;DR: In this article, a double analysis of the Value-at-Risk (VAR) indices of l'Europe du Nord and l'Eurozone du Sud is presented.
Book ChapterDOI
Catching the Drift: Using Feature-Free Case-Based Reasoning for Spam Filtering
Sarah Jane Delany,Derek Bridge +1 more
TL;DR: It is shown that a policy as simple as retaining misclassified examples has a hugely beneficial effect on handling concept drift in spam but, on its own, it results in the case base growing by over 30%.
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.