LEADER 06985nam 22007575 450 001 996465868803316 005 20200704112516.0 010 $a3-642-05224-X 024 7 $a10.1007/978-3-642-05224-8 035 $a(CKB)1000000000804409 035 $a(SSID)ssj0000355419 035 $a(PQKBManifestationID)11259247 035 $a(PQKBTitleCode)TC0000355419 035 $a(PQKBWorkID)10320110 035 $a(PQKB)10356903 035 $a(DE-He213)978-3-642-05224-8 035 $a(MiAaPQ)EBC3064739 035 $a(PPN)139962425 035 $a(EXLCZ)991000000000804409 100 $a20100301d2009 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aAdvances in Machine Learning$b[electronic resource] $eFirst Asian Conference on Machine Learning, ACML 2009, Nanjing, China, November 2-4, 2009. Proceedings /$fedited by Zhi-Hua Zhou, Takashi Washio 205 $a1st ed. 2009. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2009. 215 $a1 online resource (XV, 413 p.) 225 1 $aLecture Notes in Artificial Intelligence ;$v5828 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-642-05223-1 320 $aIncludes bibliographical references and index. 327 $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 Application to 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. 330 $aThe First Asian Conference on Machine Learning (ACML 2009) was held at Nanjing, China during November 2?4, 2009.This was the ?rst edition of a series of annual conferences which aim to provide a leading international forum for researchers in machine learning and related ?elds to share their new ideas and research ?ndings. This year we received 113 submissions from 18 countries and regions in Asia, Australasia, Europe and North America. The submissions went through a r- orous double-blind reviewing process. Most submissions received four reviews, a few submissions received ?ve reviews, while only several submissions received three reviews. Each submission was handled by an Area Chair who coordinated discussions among reviewers and made recommendation on the submission. The Program Committee Chairs examined the reviews and meta-reviews to further guarantee the reliability and integrity of the reviewing process. Twenty-nine - pers were selected after this process. To ensure that important revisions required by reviewers were incorporated into the ?nal accepted papers, and to allow submissions which would have - tential after a careful revision, this year we launched a ?revision double-check? process. In short, the above-mentioned 29 papers were conditionally accepted, and the authors were requested to incorporate the ?important-and-must?re- sionssummarizedbyareachairsbasedonreviewers?comments.Therevised?nal version and the revision list of each conditionally accepted paper was examined by the Area Chair and Program Committee Chairs. Papers that failed to pass the examination were ?nally rejected. 410 0$aLecture Notes in Artificial Intelligence ;$v5828 606 $aData mining 606 $aArtificial intelligence 606 $aOptical data processing 606 $aComputers 606 $aPattern recognition 606 $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aImage Processing and Computer Vision$3https://scigraph.springernature.com/ontologies/product-market-codes/I22021 606 $aComputer Imaging, Vision, Pattern Recognition and Graphics$3https://scigraph.springernature.com/ontologies/product-market-codes/I22005 606 $aModels and Principles$3https://scigraph.springernature.com/ontologies/product-market-codes/I18016 606 $aPattern Recognition$3https://scigraph.springernature.com/ontologies/product-market-codes/I2203X 615 0$aData mining. 615 0$aArtificial intelligence. 615 0$aOptical data processing. 615 0$aComputers. 615 0$aPattern recognition. 615 14$aData Mining and Knowledge Discovery. 615 24$aArtificial Intelligence. 615 24$aImage Processing and Computer Vision. 615 24$aComputer Imaging, Vision, Pattern Recognition and Graphics. 615 24$aModels and Principles. 615 24$aPattern Recognition. 676 $a006.3/1 702 $aZhou$b Zhi-Hua$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aWashio$b Takashi$4edt$4http://id.loc.gov/vocabulary/relators/edt 712 12$aAsian Conference on Machine Learning 906 $aBOOK 912 $a996465868803316 996 $aAdvances in Machine Learning$9773772 997 $aUNISA