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
Catching the Trend: A Framework for Clustering Concept-Drifting Categorical Data
TL;DR: A mechanism named MARDL is proposed to allocate each unlabeled data point into the corresponding appropriate cluster based on the novel categorical clustering representative, namely, N-Nodeset Importance Representative (abbreviated as NNIR), which represents clusters by the importance of the combinations of attribute values.
Journal ArticleDOI
Improving command and control speech recognition on mobile devices: using predictive user models for language modeling
TL;DR: This paper describes and assess statistical models learned from a large population of users for predicting the next user command of a commercial C&C application and investigates the effects of personalization on performance at different learning rates via online updating of model parameters based on individual user data.
Journal ArticleDOI
Evolving Fuzzy Systems.
TL;DR: With the invention of the concept of EFS, the problem of the design was completely automated and data-driven and this means, EFS systems self-develop their model as well as adapt their parameters “from scratch” on the fly using experimental data and efficient recursive learning mechanisms.
Book ChapterDOI
Tracking concept drift at feature selection stage in spamhunting: an anti-spam instance-based reasoning system
José Ramon Méndez,Florentino Fdez-Riverola,Eva Lorenzo Iglesias,Fernando Díaz,Juan M. Corchado +4 more
TL;DR: This paper shows how results obtained by a previous successful instance-based reasoning e-mail filtering system in combination with the improved feature selection method outperforms classical machine learning techniques and other well-known lazy learning approaches.
Proceedings ArticleDOI
An Incremental Learning Algorithm for Non-stationary Environments and Class Imbalance
TL;DR: This work describes and presents preliminary results for integrating SMOTE and Learn++.NSE to create an algorithm that is robust to learning in a non-stationary environment and under class imbalance.
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.