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
Bayesian Context-Dependent Learning for Anomaly Classification in Hyperspectral Imagery
TL;DR: A generic Bayesian framework is proposed for training context-dependent classification rules from wide-area airborne LWIR imagery and indicates that utilizing context for classifying anomalies in HSI could lead to more robust performance over varying terrain.
Proceedings ArticleDOI
Food Sales Prediction: "If Only It Knew What We Know"
P. Meulstee,Mykola Pechenizkiy +1 more
TL;DR: This paper presents an ensemble learning approach that employs dynamic integration of classifier for better handling of seasonal changes and fluctuations in consumer demands and demonstrates that this approach can perform better than the currently used baseline.
Proceedings ArticleDOI
Efficient class incremental learning for multi-label classification of evolving data streams
TL;DR: An algorithm which dynamically recognizes some new frequent label combinations and updates the trained classifier by class incremental learning strategy is proposed, demonstrating its better predictive performance.
Journal ArticleDOI
Early failure prediction in feature request management systems: an extended study
TL;DR: Automated failure prediction during requirements elicitation to be a promising approach for guiding requirements engineering efforts in online settings and for reasonable estimations of these two parameters, automated prediction models provide more value than a set of baselines for many failure types and projects.
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.