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
More filters
Journal ArticleDOI
An incremental learning algorithm based on the K-associated graph for non-stationary data classification
TL;DR: An extension of the K-associated optimal graph learning algorithm to cope with classification over non-stationary domains by updating its set of components when new data is presented along time, by removing old components as new components arise.
Book ChapterDOI
Distributed learning with data reduction
TL;DR: The central part of the dissertation proposes an agent-based distributed learning framework to carry-out data reduction in parallel in separate locations, employing specialized software agents.
Proceedings ArticleDOI
Automatic repairing of web wrappers
TL;DR: The proposed solution extends conventional forward wrappers with alternative classifiers built using content features of extracted information and wrappers processing pages backward to help with information extraction recovery and wrapper repairing.
Journal ArticleDOI
Tracking recurrent concepts using context
TL;DR: This work proposes the extension of existing approaches to deal with the problem of recurring concepts by reusing previously learned decision models in situations where concepts reappear and addresses the challenge of retrieving the most appropriate concept for a particular context.
Proceedings ArticleDOI
Concept Drift Awareness in Twitter Streams
TL;DR: This paper presents a learning strategy to learn with drift in the occurrence of concepts in Twitter, and proposes three different models: a time-window model, an ensemble-based model and an incremental model.
References
More filters
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.