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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It's Time to Consider "Time" when Evaluating Recommender-System Algorithms [Proposal]
TL;DR: It is proposed that recommender-system researchers should instead calculate metrics for time-series such as weeks or months, and plot the results in e.g. a line chart to show how algorithms' effectiveness develops over time, and hence the results allow drawing more meaningful conclusions about how an algorithm will perform in the future.
Proceedings ArticleDOI
Detecting Different Types of Concept Drifts with Ensemble Framework
TL;DR: Experimental results show EFDD can achieve significant improvement with p < 0.05 using z-score test when comparing to drift detection algorithms that detect only a few types of drift.
Book ChapterDOI
Handling Concept Drifts in Regression Problems -- the Error Intersection Approach
TL;DR: A novel approach for concept drift handling is explored, which depicts a strategy to switch between the application of simple and complex machine learning models for regression tasks, and is able to show that the suggested approach outperforms all regarded baselines significantly.
Journal ArticleDOI
A Collaborative Filtering Recommender System Integrated with Interest Drift based on Forgetting Function
Wu Sen,Zhang Xiaonan,Du Yannan +2 more
TL;DR: Combining user similarity index with exponential function as the improved algorithm, this paper re-computes the predicted ratings based on traditional user-based CF using Pearson Correlation Coefficient using Movielens dataset to improve user similarity calculation.
Journal ArticleDOI
Information-Theoretic Data Discarding for Dynamic Trees on Data Streams
TL;DR: This work takes steps to overcome challenges by porting information theoretic heuristics, such as exponential forgetting and active learning, into a fully Bayesian framework, by augmenting a modern non-parametric modeling framework, dynamic trees, and illustrating its performance on a number of practical examples.
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.
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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.
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.