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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Hybrid learning incorporating neural and symbolic processes
R. Sun,Todd Peterson +1 more
TL;DR: This work presents a learning model CLARION, which is a hybrid connectionist model based on the two-level approach proposed in the CONSYDERR architecture that unifies neural, reinforcement, and symbolic methods to perform online, bottom-up learning.
Journal ArticleDOI
Analysis of Recommendations from Mining Incident Investigative Reports: A 50-Year Review
TL;DR: In this paper, a systematic analysis was conducted using ten occupational health and safety commissioned reports from Canada, New Zealand, United States, United Kingdom, and Australia spanning from 1967 to 2015.
Posted Content
Addressing machine learning concept drift reveals declining vaccine sentiment during the COVID-19 pandemic
Martin Müller,Marcel Salathé +1 more
TL;DR: While vaccine sentiment has declined considerably during the COVID-19 pandemic in 2020, algorithms trained on pre-pandemic data would have largely missed this decline due to concept drift, suggesting that social media analysis systems must address concept drift in a continuous fashion in order to avoid the risk of systematic misclassification of data.
Journal ArticleDOI
A social approach for learning agents
TL;DR: It is shown in this paper that Social Network Theory can provide the multi-agent learning community with sophisticated and well-founded reputation models that outperform well-known ensemble-based drift detection techniques, generating accurate and small ensembles of learning agents.
Journal ArticleDOI
Revisiting the effect of history on learning performance: the problem of the demanding lord
TL;DR: This work proposes an analytic model that describes the effect of memory window size on the prediction performance of a learning system that is based on iterative feedback and proposes a stepping stone toward finding the memory window that maximizes the average performance for a given drift setting and a specific modeling system.
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.