Advances in Machine Learning: First Asian Conference on by Thomas G. Dietterich (auth.), Zhi-Hua Zhou, Takashi Washio

By Thomas G. Dietterich (auth.), Zhi-Hua Zhou, Takashi Washio (eds.)

The First Asian convention on computing device studying (ACML 2009) used to be held at Nanjing, China in the course of November 2–4, 2009.This used to be the ?rst variation of a sequence of annual meetings which objective to supply a number one foreign discussion board for researchers in laptop studying and similar ?elds to proportion their new rules and learn ?ndings. This yr we acquired 113 submissions from 18 international locations and areas in Asia, Australasia, Europe and North the USA. The submissions went via a r- orous double-blind reviewing method. such a lot submissions got 4 studies, a number of submissions bought ?ve reports, whereas in basic terms a number of submissions acquired 3 studies. every one submission was once dealt with through a space Chair who coordinated discussions between reviewers and made suggestion at the submission. this system Committee Chairs tested the stories and meta-reviews to additional warrantly the reliability and integrity of the reviewing procedure. Twenty-nine - pers have been chosen after this technique. to make sure that very important revisions required via reviewers have been integrated into the ?nal approved papers, and to permit submissions which might have - tential after a cautious revision, this yr we introduced a “revision double-check” approach. briefly, the above-mentioned 29 papers have been conditionally permitted, and the authors have been asked to include the “important-and-must”re- sionssummarizedbyareachairsbasedonreviewers’comments.Therevised?nal model and the revision record of every conditionally approved paper used to be tested through the world Chair and software Committee Chairs. Papers that didn't move the exam have been ?nally rejected.

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Adaptive learning from evolving data streams. : New ensemble methods for evolving data streams. In: KDD 2009. : Classification and Regression Trees. : Fast and light boosting for adaptive mining of data streams. , Zhang, C. ) PAKDD 2004. LNCS (LNAI), vol. 3056, pp. 282–292. : Improving the performance of an incremental algorithm driven by error margins. Intell. Data Anal. : Mining high-speed data streams. In: Knowledge Discovery and Data Mining, pp. : Learning with drift detection. In: SBIA Brazilian Symposium on Artificial Intelligence, pp.

LED Generator. This data source originates from the CART book [7]. An implementation in C was donated to the UCI [1] machine learning repository by David Aha. The goal is to predict the digit displayed on a seven-segment LED display, where each attribute has a 10% chance of being inverted. It has an optimal Bayes classification rate of 74%. The particular configuration of the generator used for experiments (led) produces 24 binary attributes, 17 of which are irrelevant. Data streams may be considered infinite sequences of (x, y) where x is the feature vector and y the class label.

The prices in this market are set every five minutes. The ELEC dataset contains 45, 312 instances. e. there are 48 instances for each time period of one day. The class label identifies the change of the price related to a moving average of the last 24 hours. The class level only reflect deviations of the price on a one day average and removes the impact of longer term price trends. 32 A. Bifet et al. Table 2. Comparison of algorithms. Accuracy is measured as the final percentage of examples correctly classified over the 1 or 10 million test/train interleaved evaluation.

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