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
A Hidden Markov Model for Collaborative Filtering
TL;DR: A hidden Markov model is proposed to correctly interpret the users' product selection behaviors and make personalized recommendations and it is found that the proposed HMM-based collaborative filter performs as well as the best among the alternative algorithms when the data is sparse or static.
Journal ArticleDOI
A Martingale Framework for Detecting Changes in Data Streams by Testing Exchangeability
Shen-Shyang Ho,Harry Wechsler +1 more
TL;DR: Experimental results show the feasibility and effectiveness of the martingale methodology in detecting changes in the data generating model for time-varying data streams and shows that an adaptive support vector machine utilizing the martedale methodology compares favorably against an adaptive SVM utilizing a sliding window.
Proceedings ArticleDOI
Online classification of nonstationary data streams
TL;DR: This paper presents a meta-modelling procedure that automates the very labor-intensive and therefore time-heavy and therefore expensive process of manually cataloging and classifying data.
Classifier Ensembles for Detecting Concept Change in Streaming Data: Overview and Perspectives
TL;DR: This paper outlines possibilities for novel approaches to classification of streaming data in the presence of concept change and takes a systematic look at the types of changes in streaming data and at the current approaches and techniques in online classification.
Journal ArticleDOI
Change Detection in Streaming Multivariate Data Using Likelihood Detectors
TL;DR: This paper gives a log-likelihood justification for two well-known criteria for detecting change in streaming multidimensional data: Kullback-Leibler (K-L) distance and Hotelling's T-square test for equal means (H).
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.
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.