Adaptive algorithms and stochastic approximations
Adaptive algorithms and stochastic approximations
Detection of abrupt changes: theory and application
Detection of abrupt changes: theory and application
Fast exact multiplication by the Hessian
Neural Computation
Learning in the presence of concept drift and hidden contexts
Machine Learning
Adaptive filter theory (3rd ed.)
Adaptive filter theory (3rd ed.)
Using and combining predictors that specialize
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
Machine Learning - Special issue on context sensitivity and concept drift
Parameter adaptation in stochastic optimization
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
Identification of Time-Varying Processes
Identification of Time-Varying Processes
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Models and issues in data stream systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
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
A Second-Order Perceptron Algorithm
SIAM Journal on Computing
Prediction, Learning, and Games
Prediction, Learning, and Games
SpamHunting: An instance-based reasoning system for spam labelling and filtering
Decision Support Systems
Online Passive-Aggressive Algorithms
The Journal of Machine Learning Research
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
Tracking the best hyperplane with a simple budget Perceptron
Machine Learning
From External to Internal Regret
The Journal of Machine Learning Research
The Forgetron: A Kernel-Based Perceptron on a Budget
SIAM Journal on Computing
Real-time data mining of non-stationary data streams from sensor networks
Information Fusion
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
Efficient learning algorithms for changing environments
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Statistical Analysis and Data Mining
SETN'12 Proceedings of the 7th Hellenic conference on Artificial Intelligence: theories and applications
Energy-based function to evaluate data stream clustering
Advances in Data Analysis and Classification
A similarity-based approach for data stream classification
Expert Systems with Applications: An International Journal
Data stream dynamic clustering supported by Markov chain isomorphisms
Intelligent Data Analysis
| Hi-index | 0.01 |
Streaming data introduce challenges mainly due to changing data distributions (population drift). To accommodate population drift we develop a novel linear adaptive online classification method motivated by ideas from adaptive filtering. Our approach allows the impact of past data on parameter estimates to be gradually removed, a process termed forgetting, yielding completely online adaptive algorithms. Extensive experimental results show that this approach adjusts the forgetting mechanism to maintain performance. Moreover, it might be possible to exploit the information in the evolution of the forgetting mechanism to obtain information about the type and speed of the underlying population drift process.