Kalman Filters and Adaptive Windows for Learning in Data Streams
Albert Bifet and Ricard Gavaldà
In: 9th International Conference on Discovery Sicence (DS 2006), 7-10 Oct 2006, Barcelona, Spain.
We study the combination of Kalman filter and a recently proposed algorithm for dynamically maintaining a sliding window, for learning from streams of examples. We integrate this idea into two well known learning algorithms, the Naive Bayes algorithm and the k-means clusterer. We show on synthetic data that the new algorithms do never worse, and in some cases much better, than the algorithms using only memoryless Kalman filters or sliding windows with no filtering.