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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Journal ArticleDOI
Bayes Classification for Nonstationary Patterns
TL;DR: The algorithm concept is based on the sensitivity method, used with artificial neural networks, and use of the Bayes approach minimizes the expected value of misclassifications, allowing additionally for an influence in the proportions of probability of errors when assigning to specific classes.
Proceedings ArticleDOI
ConceptExplorer: Visual Analysis of Concept Drifts in Multi-source Time-series Data
Xumeng Wang,Wei Chen,Jiazhi Xia,Zexian Chen,Dongshi Xu,Xiangyang Wu,Mingliang Xu,Tobias Schreck +7 more
TL;DR: Zhang et al. as mentioned in this paper proposed a visual analysis approach for detecting and analyzing concept drifts from multi-sourced time-series data based on prediction models and integrated visual interface, ConceptExplorer, facilitates visual exploration, extraction, understanding, and comparison of concepts and concept drift from multisource time series data.
Book ChapterDOI
Concept Neurons – Handling Drift Issues for Real-Time Industrial Data Mining
TL;DR: A generic and yet simplistic framework to fill the gap denominated Concept Neurons, which leverages on a combination of continuous inspection schemas and residual-based updates over the model parameters and/or the model output can empower the resistance of most of induction learning algorithms to concept drifts.
Dissertation
To The Knowledge Frontier and Beyond: A Hybrid System for Incremental Contextual- Learning and Prudence Analysis
TL;DR: This thesis shows that the idea of incorporating higher forms of context in symbolic reasoning domains is both possible and highly effective, vastly improving the robustness of the KBS approach.
Journal ArticleDOI
Gorthaur-EXP3: Bandit-based selection from a portfolio of recommendation algorithms balancing the accuracy-diversity dilemma
TL;DR: Gorthaur-EXP3 is an extension of the original Gorthaur method, which uses a roulette wheel selection, and obtains better results in most experimental cases, aiming at automatically selecting the optimal algorithms which best maximise global accuracy and diversity of recommendations according to a predefined trade-off.
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.
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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.