By Thomas G. Dietterich (auth.), Zhi-Hua Zhou, Takashi Washio (eds.)
The First Asian convention on desktop studying (ACML 2009) was once 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 goal to supply a number one foreign discussion board for researchers in computer studying and similar ?elds to percentage their new principles and learn ?ndings. This 12 months we obtained 113 submissions from 18 nations and areas in Asia, Australasia, Europe and North the US. The submissions went via a r- orous double-blind reviewing strategy. so much submissions obtained 4 reports, a couple of submissions obtained ?ve experiences, whereas basically a number of submissions obtained 3 stories. every 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 reports and meta-reviews to extra warrantly the reliability and integrity of the reviewing method. Twenty-nine - pers have been chosen after this technique. to make sure that vital revisions required via reviewers have been integrated into the ?nal authorized papers, and to permit submissions which might have - tential after a cautious revision, this yr we introduced a “revision double-check” method. 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 checklist of every conditionally authorized paper was once tested through the realm Chair and application Committee Chairs. Papers that did not move the exam have been ?nally rejected.
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Additional resources for Advances in Machine Learning: First Asian Conference on Machine Learning, ACML 2009, Nanjing, China, November 2-4, 2009. Proceedings
Traces are generated by generating plans from the given initial and goal states in these planning domains using the human encoded action models and a planning algorithm, FF planner 3 . These domains have the characteristics we need to evaluate our t-LAMP algorithm: they have enough similarities and hence we have the intuition that one can borrow knowledge from the other while learning the action models (as shown in the examples of earlier sections). Error rates are defined as the differences between our learned action models and the hand-written action models that are considered as the “ground truth” from IPC-2.
Each rating matrix can thus be viewed as drawing a set of users Uz and items Vz from the user-item joint mixture model as well as drawing the corresponding ratings for (Uz , Vz ) from the cluster-level rating model. To enable the learning to happen, we can adopt the Expectation Maximization (EM) algorithm for RMGM training on z Dz . , P (cU ), P (cV ), P (u|cU ), P (v|cV ), and P (r|cU , cV ), In the E(k) (l) (z) (z) (z) step, the joint posterior probability P (cU , cV |ui , vi , ri ) can be computed using the above five sets of model parameters.
For each point i in the sequence that is being sampled, the error rate is the probability of misclassifying (pi ), with standard deviation given by si = pi (1 − pi )/i. They assume (as stated in the PAC learning model ) that the error rate of the learning algorithm (pi ) will decrease while the number of examples increases if the distribution of the examples is stationary. A signiﬁcant increase in the error of the algorithm, suggests that the class distribution is changing and, hence, the actual decision model is supposed to be inappropriate.