By Carole Goble, Oscar Corcho, Pinar Alper, David De Roure (auth.), Ljupčo Todorovski, Nada Lavrač, Klaus P. Jantke (eds.)
The ninth foreign convention on Discovery technological know-how (DS 2006) used to be held in Barcelona, Spain, on 7–10 October 2006. The convention was once collocated with the seventeenth overseas convention on Algorithmic studying thought (ALT 2006). the 2 meetings shared the invited talks. This LNAI quantity, containing the complaints of the ninth overseas C- ference onDiscoveryScience, is based in 3 components. The ?rstpart includes the papers/abstracts of the invited talks, the second one half comprises the authorised lengthy papers, and the 3rd half the permitted general (short) papers. Out of 87 submitted papers, 23 have been authorized for ebook as lengthy papers, and 18 as common papers. all of the submitted papers have been reviewed by means of or 3 ref- ees. as well as the shows of approved papers, the DS 2006 convention application consisted of 3 invited talks, tutorials, the collocated ALT 2006 convention and the Pascal Dialogues workshop. we want to show our gratitude to – the authors of submitted papers, – this system committee and different referees for his or her thorough and well timed paper review, – DS 2006 invited audio system Carole Goble and Padhraic Smyth, in addition to - drew Ng as joint DS 2006 and ALT 2006 invited speaker, – invited instructional audio system Luis Torgo and Michael may possibly, – the neighborhood association committee chaired through Ricard Gavalda, ` – DS 2006 convention chair Klaus P.
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Extra resources for Discovery Science: 9th International Conference, DS 2006, Barcelona, Spain, October 7-10, 2006. Proceedings
We use synthetic data in order to control precisely the type and amount of distribution drift. The main conclusions are: Kalman Filters and Adaptive Windows for Learning in Data Streams 31 – In all three types of experiments (tracking, Na¨ıve Bayes, and k-means), K-ADWIN either gives best results or is very close in performance to the best of the estimators we try. And each of the other estimators is clearly outperformed by K-ADWIN in at least some of the experiments. In other words, no estimator ever does much better than K-ADWIN, and each of the others is outperformed by K-ADWIN in at least one context.
Edu Abstract. 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 wellknown learning algorithms, the Na¨ıve 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. 1 Introduction We deal with the problem of distribution and concept drift when learning from streams of incoming data.
We study the combination of a classical estimation method in automatic control theory, the Kalman ﬁlter, with the also classical idea in machine learning of using a window of recently seen data items for learning. Many of the previous works in the machine learning area use windows of a ﬁxed length. We use instead an algorithm that we proposed recently  for adaptively changing the size of the window in reaction to changes observed in the data. In automatic control theory, many modern complex systems may be classed as estimation systems, combining several sources of (often redundant) data in order to arrive at an estimate of some unknown parameters.
Discovery Science: 9th International Conference, DS 2006, Barcelona, Spain, October 7-10, 2006. Proceedings by Carole Goble, Oscar Corcho, Pinar Alper, David De Roure (auth.), Ljupčo Todorovski, Nada Lavrač, Klaus P. Jantke (eds.)