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
Learning Rules from User Behaviour
TL;DR: This paper presents a novel, logic-based approach for automatically learning and updating models of users from their observed behaviour, using a nonmonotonic learning system and illustrates how the approach can be exploited within a pervasive computing framework.
Journal ArticleDOI
Online cost-sensitive neural network classifiers for non-stationary and imbalanced data streams
TL;DR: In the proposed classifiers, class imbalance is handled with two separate cost-sensitive strategies, the first one incorporates a fixed and the second one an adaptive misclassification cost matrix.
Book ChapterDOI
Online non-stationary boosting
TL;DR: This work presents an algorithm called Online Non-Stationary Boosting (ONSBoost) that, like Online Boosting, uses a static ensemble size without generating new members each time new examples are presented, and also adapts to a changing data distribution.
Journal ArticleDOI
Autopoiesis, the immune system, and adaptive information filtering
Nikolaos Nanas,Anne De Roeck +1 more
TL;DR: Nootropia, a model inspired by the autopoietic view of the immune system that reacts to user feedback in order to define and preserve the user interests, is described in the context of adaptive, content-based document filtering and evaluated using virtual users.
Adaptive parameter-free learning from evolving data streams
TL;DR: A method for developing algorithms that can adaptively learn from data streams that change over time, based on using change detectors and estimator modules at the right places and choosing implementations with theoretical guarantees in order to extend such guarantees to the resulting adaptive learning algorithm.
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.