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 hidden Markov model for collaborative filtering
TL;DR: A hidden Markov model is proposed to correctly interpret the users' product selection behaviors and make personalized recommendations and it is found that the proposed HMM-based collaborative filter performs as well as the best among the alternative algorithms when the data is sparse or static.
Proceedings ArticleDOI
OPPORTUNITY: Towards opportunistic activity and context recognition systems
Daniel Roggen,Kilian Förster,Alberto Calatroni,Thomas Holleczek,Yu Fang,Gerhard Tröster,Alois Ferscha,Clemens Holzmann,Andreas Riener,Paul Lukowicz,Gerald Pirkl,David Bannach,Kai Kunze,Ricardo Chavarriaga,José del R. Millán +14 more
TL;DR: The newly started European research project OPPORTUNITY is introduced within which mobile opportunistic activity and context recognition systems are developed within which the approach is followed along opportunistic sensing, data processing and interpretation, and autonomous adaptation and evolution to environmental and user changes.
Journal ArticleDOI
Novelty detection in data streams
TL;DR: Different aspects of novelty detection in data streams, like the offline and online phases, the number of classes considered at each phase, the use of ensemble versus a single classifier, supervised and unsupervised approaches for the learning task, and how to deal with recurring classes are presented.
Book ChapterDOI
Classification of EEG for Affect Recognition: An Adaptive Approach
TL;DR: The results show that affect recognition from EEG signals might be possible and an adaptive algorithm improves the performance of the classification task.
Journal ArticleDOI
Fuzzily Connected Multimodel Systems Evolving Autonomously From Data Streams
TL;DR: The main thrust of this paper is in the development of an original approach for the self-design, self-monitoring,self-management, and self-learning of such systems in a dynamic manner from data streams which automatically detect and react to the shift in the data distribution by evolving the system structure.
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.
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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.
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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.
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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.