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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Helping everyday users find anomalies in data feeds
TL;DR: The research presented here addresses this problem by providing CUES, Checking User Expectations about Semantics, a method and a prototype implementation for making user expectations precise and for checking these precise expectations to detect semantic anomalies in data feeds.
Book ChapterDOI
An Online Learning-Based Adaptive Biometric System
TL;DR: This chapter lists and discusses a few out of many potential learning techniques that can be applied to build an adaptive biometric system and builds an adaptiveBiometric system to illustrate the efficacy of one of the incremental learning techniques from the literature.
Proceedings ArticleDOI
A cellular automata approach to detecting concept drift and dealing with noise
TL;DR: Experiments show that a good choice of local rules for CA can reduce the concept convergence time considerably and increase model robustness to noise; thus presenting a more accurate stream-learning.
Journal ArticleDOI
Learning Concept Drift Using Adaptive Training Set Formation Strategy
Nabil M. Hewahi,Sarah Kohail +1 more
TL;DR: The authors propose an adaptive supervised learning with delayed labeling methodology which is considered as the first systematic training set formation approach which takes into account delayed labeling problem and indicates the effectiveness of the proposed method over other methods in terms of classification accuracy.
Book ChapterDOI
An analysis of change trends by predicting from a data stream using genetic fuzzy systems
TL;DR: The results proved the usefulness of ensemble approach incorporating the correction of individual component model output and the impact of different trend functions on the accuracy of single and ensemble fuzzy models was investigated.
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.
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.