System identification: theory for the user
System identification: theory for the user
Adaptation and tracking in system identification—a survey
Automatica (Journal of IFAC) - Identification and system parameter estimation
Adaptive algorithms and stochastic approximations
Adaptive algorithms and stochastic approximations
Learning in the presence of concept drift and hidden contexts
Machine Learning
Adaptive filter theory (3rd ed.)
Adaptive filter theory (3rd ed.)
Bayesian forecasting and dynamic models (2nd ed.)
Bayesian forecasting and dynamic models (2nd ed.)
A Bayesian approach to on-line learning
On-line learning in neural networks
The impact of changing populations on classifier performance
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining time-changing data streams
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
A streaming ensemble algorithm (SEA) for large-scale classification
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Identification of Time-Varying Processes
Identification of Time-Varying Processes
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
A Comparison of Batch and Incremental Supervised Learning Algorithms
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Convergence Analysis of Online Linear Discriminant Analysis
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 3 - Volume 3
Mining concept-drifting data streams using ensemble classifiers
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
On demand classification of data streams
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
SpamHunting: An instance-based reasoning system for spam labelling and filtering
Decision Support Systems
A framework for generating data to simulate changing environments
AIAP'07 Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: artificial intelligence and applications
Real-time data mining of non-stationary data streams from sensor networks
Information Fusion
Dynamic Weighted Majority: An Ensemble Method for Drifting Concepts
The Journal of Machine Learning Research
Adaptive Learning Rate for Online Linear Discriminant Classifiers
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Online optimization for variable selection in data streams
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
ROC Curves for Continuous Data
ROC Curves for Continuous Data
On the window size for classification in changing environments
Intelligent Data Analysis
On-line estimation with the multivariate Gaussian distribution
COLT'07 Proceedings of the 20th annual conference on Learning theory
Assessing the impact of changing environments on classifier performance
Canadian AI'08 Proceedings of the Canadian Society for computational studies of intelligence, 21st conference on Advances in artificial intelligence
λ-Perceptron: An adaptive classifier for data streams
Pattern Recognition
An adaptive nearest neighbor classification algorithm for data streams
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Time-varying spectral estimation using AR models with variableforgetting factors
IEEE Transactions on Signal Processing
Incremental linear discriminant analysis for classification of data streams
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Brief paper: Implementation of self-tuning regulators with variable forgetting factors
Automatica (Journal of IFAC)
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Advances in data technology have enabled streaming acquisition of real-time information in a wide range of settings, including consumer credit, electricity consumption, and internet user behavior. Streaming data consist of transiently observed, temporally evolving data sequences, and poses novel challenges to statistical analysis. Foremost among these challenges are the need for online processing, and temporal adaptivity in the face of unforeseen changes, both smooth and abrupt, in the underlying data generation mechanism. In this paper, we develop streaming versions of two widely used parametric classifiers, namely quadratic and linear discriminant analysis. We rely on computationally efficient, recursive formulations of these classifiers. We additionally equip them with exponential forgetting factors that enable temporal adaptivity via smoothly down-weighting the contribution of older data. Drawing on ideas from adaptive filtering, we develop an online method for self-tuning forgetting factors on the basis of an approximate gradient scheme. We provide extensive simulation and real data analysis that demonstrate the effectiveness of the proposed method in handling diverse types of change, while simultaneously offering monitoring capabilities via interpretable behavior of the adaptive forgetting factors. © 2012 Wiley Periodicals, Inc. Statistical Analysis and Data Mining, 2012 © 2012 Wiley Periodicals, Inc.