Zhou / Washio Advances in Machine Learning

First Asian Conference on Machine Learning, ACML 2009, Nanjing, China, November 2-4, 2009. Proceedings
2009
ISBN: 978-3-642-05224-8
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 413 Seiten, eBook

Reihe: Lecture Notes in Artificial Intelligence

The 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.

Zielgruppe

Research

Autoren/Hrsg.

Zhou, Zhi-Hua

Washio, Takashi

Inhaltsverzeichnis

Keynote 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.