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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On the classification of dynamical data streams using novel “Anti-Bayesian” techniques
TL;DR: This paper pioneers the use of unreported novel schemes that can classify such dynamical data streams by invoking the recently-introduced “Anti-Bayesian” (AB) techniques by applying the efficient and robust quantile estimators developed by Yazidi and Hammer.
Proceedings ArticleDOI
Semi-supervised incremental learning
TL;DR: The paper introduces a hybrid evolving architecture for dealing with incremental learning that consists of two components: resource allocating neural network (RAN) and growing Gaussian mixture model (GGMM).
Proceedings ArticleDOI
Change detection in data streams through unsupervised learning
Guénaël Cabanes,Younès Bennani +1 more
TL;DR: This paper proposes a method of synthetic representation of the data structure for efficient storage of information, and a measure of dissimilarity between these representations for the detection of change in the stream structure.
Journal ArticleDOI
The importance of generalizability for anomaly detection
TL;DR: This article confirms that in anomaly detection as in other forms of classification a tight fit does not supersede model generality and is shown using three systems each with a different geometric bias in the decision space.
Journal ArticleDOI
Adaptive and Reinforcement Learning Approaches for Online Network Monitoring and Analysis
TL;DR: ADAM & RAL are applied to the real-time detection of network attacks in Internet network traffic, and it is shown that it is possible to continuously achieve high detection accuracy even under the occurrence of concept drifts, limiting the amount of labeled data needed for learning.
References
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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.
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.