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
Evolutionary Online Data Mining: An Investigation in a Dynamic Environment
TL;DR: This chapter investigates XCS, an evolutionary learning classifier system, that offers an incremental learning ability and also is able to handle an infinite amount of continuously arriving data, in dynamic environments, in the presence of noise in the training data.
Journal ArticleDOI
Hoeffding Tree Algorithms for Anomaly Detection in Streaming Datasets: A Survey
TL;DR: An extensive and well-constructed overview of using machine learning for the problem of detecting anomalies in streaming datasets and shows how a combination of techniques from different compositions can solve a prominent problem, anomaly detection.
Book ChapterDOI
Adaptation to Drifting Concepts
TL;DR: This paper presents a method for handling concept drift based on Shewhart P-Charts in an on-line framework for supervised learning, and explores the use of two alternatives P-charts, which differ only by the way they estimate the target value to set the center line.
Book ChapterDOI
An Enhanced Cyber Attack Attribution Framework
Nikolaos Pitropakis,Emmanouil Panaousis,Alkiviadis Giannakoulias,George Kalpakis,Rodrigo Diaz Rodriguez,Panayiotis Sarigiannidis +5 more
TL;DR: The Enhanced Cyber Attack Attribution (NEON) Framework is proposed, which performs attribution of malicious parties behind APT campaigns and is designed to increase societal resiliency to APTs.
Proceedings ArticleDOI
genSpace: Exploring social networking metaphors for knowledge sharing and scientific collaborative work
TL;DR: This work investigates social networking models as an approach to scientific knowledge sharing, and presents an implementation called genSpace, which is built as an extension to the geWorkbench platform for computational biologists.
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.