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 ArticleDOI
weHelp: A Reference Architecture for Social Recommender Systems.
TL;DR: WeHelp is introduced: a reference architecture for social recommender systems - systems where recommendations are derived automatically from the aggregate of logged activities conducted by the system's users, designed to be application and domain agnostic.
Journal ArticleDOI
Personalized change awareness: Reducing information overload in loosely-coupled teamwork
TL;DR: Personalized Change Awareness, a new approach for supporting team coordination which aims to automatically identify and share the subset of information about others' activities that is most relevant to each of the team members, is presented.
Proceedings ArticleDOI
Manifold regularization for semi-supervised sequential learning
Yvonne Moh,Joachim M. Buhmann +1 more
TL;DR: This work combines manifold regularization with sequential learning under a semi-supervised learning scenario, and the online learning mechanism integrates a regularization based on the data smoothness assumptions.
Proceedings ArticleDOI
Mining concept-drifting data streams containing labeled and unlabeled instances
TL;DR: A new approach for handling concept-drifting data streams containing labeled and unlabeled instances is proposed, using KL divergence and bootstrapping method to quantify and detect three possible kinds of drift: feature, conditional or dual.
Journal ArticleDOI
Adaptive Machine Learning for Automated Modeling of Residential Prosumer Agents
TL;DR: An approach is proposed to adapt the prosumer agent models to overcome the gradual and sudden concept drift concurrently and is capable of constructing efficient prosumer agents models with regard to the concept drift problem.
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.