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020 _a9783642052248
_9978-3-642-05224-8
024 7 _a10.1007/978-3-642-05224-8
_2doi
050 4 _aQA76.9.D343
072 7 _aUNF
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082 0 4 _a006.312
_223
245 1 0 _aAdvances in Machine Learning
_h[electronic resource] :
_bFirst Asian Conference on Machine Learning, ACML 2009, Nanjing, China, November 2-4, 2009. Proceedings /
_cedited by Zhi-Hua Zhou, Takashi Washio.
250 _a1st ed. 2009.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2009.
300 _aXV, 413 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aLecture Notes in Artificial Intelligence,
_x2945-9141 ;
_v5828
505 0 _aKeynote and Invited Talks -- Machine Learning and Ecosystem Informatics: Challenges and Opportunities -- Density Ratio Estimation: A New Versatile Tool for Machine Learning -- Transfer Learning beyond Text Classification -- Regular Papers -- Improving Adaptive Bagging Methods for Evolving Data Streams -- A Hierarchical Face Recognition Algorithm -- Estimating Likelihoods for Topic Models -- Conditional Density Estimation with Class Probability Estimators -- Linear Time Model Selection for Mixture of Heterogeneous Components -- Max-margin Multiple-Instance Learning via Semidefinite Programming -- A Reformulation of Support Vector Machines for General Confidence Functions -- Robust Discriminant Analysis Based on Nonparametric Maximum Entropy -- Context-Aware Online Commercial Intention Detection -- Feature Selection via Maximizing Neighborhood Soft Margin -- Accurate Probabilistic Error Bound for Eigenvalues of Kernel Matrix -- Community Detection on Weighted Networks: A Variational Bayesian Method -- Averaged Naive Bayes Trees: A New Extension of AODE -- Automatic Choice of Control Measurements -- Coupled Metric Learning for Face Recognition with Degraded Images -- Cost-Sensitive Boosting: Fitting an Additive Asymmetric Logistic Regression Model -- On Compressibility and Acceleration of Orthogonal NMF for POMDP Compression -- Building a Decision Cluster Forest Model to Classify High Dimensional Data with Multi-classes -- Query Selection via Weighted Entropy in Graph-Based Semi-supervised Classification -- Learning Algorithms for Domain Adaptation -- Mining Multi-label Concept-Drifting Data Streams Using Dynamic Classifier Ensemble -- Learning Continuous-Time Information Diffusion Model for Social Behavioral Data Analysis -- Privacy-Preserving Evaluation of Generalization Error and Its Applicationto Model and Attribute Selection -- Coping with Distribution Change in the Same Domain Using Similarity-Based Instance Weighting -- Monte-Carlo Tree Search in Poker Using Expected Reward Distributions -- Injecting Structured Data to Generative Topic Model in Enterprise Settings -- Weighted Nonnegative Matrix Co-Tri-Factorization for Collaborative Prediction.
520 _aThe First Asian Conference on Machine Learning (ACML 2009) was held at Nanjing, China during November 2–4, 2009.This was the ?rst edition of a series of annual conferences which aim to provide a leading international forum for researchers in machine learning and related ?elds to share their new ideas and research ?ndings. This year we received 113 submissions from 18 countries and regions in Asia, Australasia, Europe and North America. The submissions went through a r- orous double-blind reviewing process. Most submissions received four reviews, a few submissions received ?ve reviews, while only several submissions received three reviews. Each submission was handled by an Area Chair who coordinated discussions among reviewers and made recommendation on the submission. The Program Committee Chairs examined the reviews and meta-reviews to further guarantee the reliability and integrity of the reviewing process. Twenty-nine - pers were selected after this process. To ensure that important revisions required by reviewers were incorporated into the ?nal accepted papers, and to allow submissions which would have - tential after a careful revision, this year we launched a “revision double-check” process. In short, the above-mentioned 29 papers were conditionally accepted, and the authors were requested to incorporate the “important-and-must”re- sionssummarizedbyareachairsbasedonreviewers’comments.Therevised?nal version and the revision list of each conditionally accepted paper was examined by the Area Chair and Program Committee Chairs. Papers that failed to pass the examination were ?nally rejected.
650 0 _aData mining.
650 0 _aArtificial intelligence.
650 0 _aComputer vision.
650 0 _aImage processing
_xDigital techniques.
650 0 _aComputer science.
650 0 _aPattern recognition systems.
650 1 4 _aData Mining and Knowledge Discovery.
650 2 4 _aArtificial Intelligence.
650 2 4 _aComputer Vision.
650 2 4 _aComputer Imaging, Vision, Pattern Recognition and Graphics.
650 2 4 _aModels of Computation.
650 2 4 _aAutomated Pattern Recognition.
700 1 _aZhou, Zhi-Hua.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aWashio, Takashi.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783642052231
776 0 8 _iPrinted edition:
_z9783642052255
830 0 _aLecture Notes in Artificial Intelligence,
_x2945-9141 ;
_v5828
856 4 0 _uhttps://doi.org/10.1007/978-3-642-05224-8
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