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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An automatic construction and organization strategy for ensemble learning on data streams
Yi Zhang,Xiaoming Jin +1 more
TL;DR: An ensemble learning algorithm is proposed, which furnishes training data for basic classifiers, starting from the up-to-date data chunk and searching for complement from past chunks while ruling out the data inconsistent with current concept and provides effective voting by adaptively distinguishing sensible classifiers from the else and engaging sensible ones as voters.
Journal ArticleDOI
An overview on the exploitation of time in collaborative filtering
TL;DR: This overview of a new generation of CF algorithms, using the time dimension as a key factor to improve recommendation models, is presented and important challenges to be faced in the near future are identified.
Proceedings ArticleDOI
Discovering decision rules from numerical data streams
TL;DR: A scalable learning algorithm to classify numerical, low dimensionality, high-cardinality, time-changing data streams, named SCALLOP provides a set of decision rules on demand which improves its simplicity and helpfulness for the user.
Proceedings Article
Temporal dynamics of user interests in tagging systems
TL;DR: This paper investigates the temporal dynamics of user interests in tagging systems and proposes a user-tag-specific temporal interests model for tracking users' interests over time and results show that this method can outperform state-of-the-art tag prediction algorithms.
Journal ArticleDOI
One-class learning and concept summarization for data streams
TL;DR: A new research problem of concept learning and summarization for one-class data streams is formulated to allow users to label instance groups, instead of single instances, as positive samples for learning, and to summarize concepts labeled by users over the whole stream.
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.
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.