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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Book ChapterDOI
Business process simulation survival guide
TL;DR: This chapter introduces simulation as an analysis tool for business process management and advocates the use of process mining techniques for creating more reliable simulation models based on real event data.
Journal ArticleDOI
An adaptive classification system for video-based face recognition
TL;DR: Simulation results indicate that the proposed adaptive classification system (ACS) can provide a significant higher classification rate than that of fuzzy ARTMAP alone during incremental learning, however, optimization of ACS parameters requires more resources.
Journal ArticleDOI
IBLStreams: a system for instance-based classification and regression on data streams
Ammar Shaker,Eyke Hüllermeier +1 more
TL;DR: The main methodological concepts underlying this approach to learning on data streams are introduced and its implementation under the MOA software framework is discussed and it turns out to be competitive to state-of-the-art methods in terms of prediction accuracy.
Journal ArticleDOI
Hidost: a static machine-learning-based detector of malicious files
Nedim źRndić,Pavel Laskov +1 more
TL;DR: Hidost is introduced, the first static machine-learning-based malware detection system designed to operate on multiple file formats and outperformed all antivirus engines deployed by the website VirusTotal to detect the highest number of malicious PDF files and ranked among the best on SWF malware.
Journal ArticleDOI
Chaff from the Wheat: Characterizing and Determining Valid Bug Reports
TL;DR: The textual features of a bug report and reporter's experience are the most important factors to distinguish valid bug reports from invalid ones, and the approach statistically significantly outperforms two baselines using features proposed by Zanetti et al.
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.