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
I-TRUST: investigating trust between users and agents in a multi-agent portfolio management system
TL;DR: The preliminary results of how reinforcement-learning agents (i.e. broker agents, or brokers) win the trust of their client in an artificial market I-TRUST are reported, which has incorporated agents’ cooperative reinforcement learning to adjust their portfolio selecting strategy, which is implemented in FIPA-OS.
Journal ArticleDOI
Leveraging microblogging big data with a modified density-based clustering approach for event awareness and topic ranking
Chung-Hong Lee,Tzan-Feng Chien +1 more
TL;DR: An online text-stream clustering approach using a modified density-based clustering model with collected microblogging big data is developed, which provides functions for recommending top-priority event information to assist people to effectively organize emerging event data through the developed topic ranking algorithm.
Book ChapterDOI
Modeling concept drift: A probabilistic graphical model based approach
Hanen Borchani,Ana M. Martínez,Andrés R. Masegosa,Helge Langseth,Thomas D. Nielsen,Antonio Salmerón,Antonio Fernández,Anders L. Madsen,Ramón Sáez +8 more
TL;DR: This paper proposes a framework, based on probabilistic graphical models, that explicitly represents concept drift using latent variables, and resorts to a variational Bayes inference scheme to ensure efficient inference and learning.
Proceedings ArticleDOI
Adaptive Local Learning Soft Sensor for Inferential Control Support
Petr Kadlec,Bogdan Gabrys +1 more
TL;DR: This work focused on the development of an adaptive Soft Sensor which may be deployed in a real-life environment, for example as inferential control support, by training a set of models with limited validity in the data space and proposing a statistically-based technique for the combination of the local models.
Journal ArticleDOI
Self-Adaptive Induction of Regression Trees
R Fidalgo-Merino,Marlon Núñez +1 more
TL;DR: The proposed algorithm, called SAIRT, adapts the induced model when facing data streams involving unknown dynamics, like gradual and abrupt function drift, changes in certain regions of the function, noise, and virtual drift, and it also handles both symbolic and numeric attributes.
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.
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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.