By Thomas G. Dietterich (auth.), Zhi-Hua Zhou, Takashi Washio (eds.)
The First Asian convention on computer studying (ACML 2009) used to be held at Nanjing, China in the course of November 2–4, 2009.This used to be the ?rst version of a chain of annual meetings which target to supply a number one overseas discussion board for researchers in desktop studying and similar ?elds to proportion their new principles and examine ?ndings. This yr we obtained 113 submissions from 18 nations and areas in Asia, Australasia, Europe and North the United States. The submissions went via a r- orous double-blind reviewing strategy. such a lot submissions obtained 4 experiences, a couple of submissions bought ?ve stories, whereas basically numerous submissions got 3 experiences. each one submission used to be dealt with through a space Chair who coordinated discussions between reviewers and made advice at the submission. this system Committee Chairs tested the studies and meta-reviews to extra warrantly the reliability and integrity of the reviewing technique. Twenty-nine - pers have been chosen after this technique. to make sure that vital revisions required via reviewers have been integrated into the ?nal authorised papers, and to permit submissions which might have - tential after a cautious revision, this 12 months we introduced a “revision double-check” technique. briefly, the above-mentioned 29 papers have been conditionally authorized, 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 authorised paper was once tested through the world Chair and software Committee Chairs. Papers that didn't go the exam have been ?nally rejected.
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Extra info for Advances in Machine Learning: First Asian Conference on Machine Learning, ACML 2009, Nanjing, China, November 2-4, 2009. Proceedings
LED Generator. This data source originates from the CART book . An implementation in C was donated to the UCI  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.
Using the Hoeﬀding bound one can show that the output of a Hoeﬀding tree is asymptotically nearly identical to that of a non-incremental learner using inﬁnitely many examples. See  for details. The Adaptive-Size Hoeﬀding Tree (ASHT) is derived from the Hoeﬀding Tree algorithm with the following diﬀerences: – it has a value for the maximum number of split nodes, or size – after one node splits, if the number of split nodes of the ASHT tree is higher than the maximum value, then it deletes some nodes to reduce its size When the tree size exceeds the maximun size value, there are two diﬀerent delete options: – delete the oldest node, the root, and all of its children except the one where the split has been made.
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Advances in Machine Learning: First Asian Conference on Machine Learning, ACML 2009, Nanjing, China, November 2-4, 2009. Proceedings by Thomas G. Dietterich (auth.), Zhi-Hua Zhou, Takashi Washio (eds.)