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
A Survey of Learning Causality with Data: Problems and Methods
TL;DR: This survey provides a comprehensive and structured review of both traditional and frontier methods in learning causal effects and relations along with the connections between causality and machine learning.
Proceedings ArticleDOI
Web-page classification through summarization
TL;DR: This paper gives empirical evidence that ideal Web-page summaries generated by human editors can indeed improve the performance of Web- page classification algorithms and proposes a new Web summarization-based classification algorithm that achieves an approximately 8.8% improvement over pure-text based methods.
Journal ArticleDOI
A case-based technique for tracking concept drift in spam filtering
TL;DR: This paper presents a case-based system for spam filtering that can learn dynamically and explores the benefit of periodically redoing the feature selection process to bring new features into play.
Book
Data Mining in Time Series Databases
Horst Bunke,Abraham Kandel +1 more
TL;DR: A Survey of Recent Methods for Efficient Retrieval of Similar Time Sequences and Change Detection in Classification Models of Data Mining.
Journal ArticleDOI
Evolving fuzzy classifiers using different model architectures
TL;DR: A special emphasis is placed on advanced issues for improving accuracy and robustness, including a thorough comparison between global and local learning of consequent functions, a novel approach for detecting of and reacting on drifts in the data streams and an enhanced outlier treatment strategy.
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.