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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Book ChapterDOI
Handling Concept Drift
TL;DR: This chapter presents the different methods and techniques used to learn from data streams in evolving and nonstationary environments, and their performances will be compared according to the generated drift characteristics.
Proceedings ArticleDOI
MDINFERENCE: Balancing Inference Accuracy and Latency for Mobile Applications
Samuel S. Ogden,Tian Guo +1 more
TL;DR: In this article, the authors proposed a holistic approach to designing mobile deep inference frameworks, which leverages two complementary techniques; a model selection algorithm that chooses from a set of cloud-based deep learning models to improve inference accuracy and an on-device request duplication mechanism to bound latency.
Proceedings ArticleDOI
A GA-based approach for mining membership functions and concept-drift patterns
TL;DR: This paper proposes a GA-based approach for mining fuzzy concept-drift patterns that consists of appropriate membership functions for items derived by GA with a designed fitness function.
Journal ArticleDOI
Inference From Aging Information
TL;DR: It was found that the algebraic tails of this posterior distribution give the learning algorithm the ability to cope with an evolving environment by permitting the escape from local traps.
Proceedings ArticleDOI
A Pdf-Free Change Detection Test for Data Streams Monitoring
Li Bu,Dongbin Zhao,Cesare Alippi +2 more
TL;DR: A novel change detection test based on the least squares density difference estimation, which requires limited data to become operational and thresholds needed to assess the change can be set met to predefined false positive rates.
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.
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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.
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.