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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Proceedings Article
We're Not in Kansas Anymore: Detecting Domain Changes in Streams
TL;DR: This paper empirically show effective domain shift detection on a variety of data sets and shift conditions, and uses A-distance, a metric for detecting shifts in data streams, combined with classification margins to detect domain shifts.
Journal ArticleDOI
Supervised domain adaptation of decision forests: Transfer of models trained in vitro for in vivo intravascular ultrasound tissue characterization
Sailesh Conjeti,Amin Katouzian,Abhijit Guha Roy,Loïc Peter,Debdoot Sheet,Stephane Carlier,Andrew F. Laine,Nassir Navab,Nassir Navab +8 more
TL;DR: A novel method for DA is introduced through an error-correcting hierarchical transfer relaxation scheme with domain alignment, feature normalization, and leaf posterior reweighting to correct for the distribution shift between the domains.
Journal ArticleDOI
A cognitive robotic ecology approach to self-configuring and evolving AAL systems
Mauro Dragone,Giuseppe Amato,Davide Bacciu,Stefano Chessa,Sonya Coleman,Maurizio Di Rocco,Claudio Gallicchio,Claudio Gennaro,Hector Lozano,Liam Maguire,Martin McGinnity,Alessio Micheli,Gregory M. P. O'Hare,Arantxa Renteria,Alessandro Saffiotti,Claudio Vairo,Philip Vance +16 more
TL;DR: This paper demonstrates how the integrated and self-organising cognitive solutions implemented within the EU project RUBICON (Robotic UBIquitous Cognitive Network) can reduce the need of costly pre-programming and maintenance of robotic ecologies.
Proceedings ArticleDOI
Novel class detection in concept-drifting data stream mining employing decision tree
TL;DR: The proposed approach for incremental learning of concept drift considers mining, where the streaming data distributions change over time, and build a decision tree model from training dataset, which continuously updates so that the tree represents the most recent concept in data stream.
Book ChapterDOI
On dynamic feature weighting for feature drifting data streams
TL;DR: Insight is provided into how the relevance of features can be tracked as a stream progresses according to information theoretical Symmetrical Uncertainty and how it can be used to boost two learning schemes: Naive Bayesian and k-Nearest Neighbor.
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.
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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.