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
Solving Nonstationary Classification Problems With Coupled Support Vector Machines
TL;DR: This work introduces the time-adaptive support vector machine (TA-SVM), a new method for generating adaptive classifiers, capable of learning concepts that change with time, and analyzes the regularizing effect of changing the number of classifiers in the sequence or adapting the strength of the coupling.
Journal ArticleDOI
KSAP: An approach to bug report assignment using KNN search and heterogeneous proximity
Wen Zhang,Song Wang,Qing Wang +2 more
TL;DR: This is the first paper to demonstrate how to automatically build heterogeneous network of a bug repository and extract meta-paths of developer collaborations from theheterogeneous network for bug report assignment.
Proceedings Article
Continual Learning with Bayesian Neural Networks for Non-Stationary Data
TL;DR: This work addresses continual learning for non-stationary data, using Bayesian neural networks and memory-based online variational Bayes, by introducing a novel method for sequentially updating both components of the posterior approximation.
Proceedings ArticleDOI
An ensemble based incremental learning framework for concept drift and class imbalance
Gregory Ditzler,Robi Polikar +1 more
TL;DR: Two modified frameworks for an algorithm that can be used to incrementally learn from imbalanced data coming from a nonstationary environment are proposed.
Proceedings ArticleDOI
Tracking concept drift in malware families
TL;DR: This paper investigates the assumption that malware population is stationary i.e. probability distribution of the observed characteristics of malware populations don't change over time, and proposes two measures for tracking concept drift in malware families when feature sets are very large-relative temporal similarity and metafeatures.
References
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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.