Advances in Machine Learning

1. Front Matter

   PDF

2. Keynote and Invited Talks

   1. Machine Learning and Ecosystem Informatics: Challenges and Opportunities

      • Thomas G. Dietterich

      Pages 1-5

   2. Density Ratio Estimation: A New Versatile Tool for Machine Learning

      • Masashi Sugiyama

      Pages 6-9

   3. Transfer Learning beyond Text Classification

      • Qiang Yang

      Pages 10-22

3. Regular Papers

   1. Improving Adaptive Bagging Methods for Evolving Data Streams

      • Albert Bifet, Geoff Holmes, Bernhard Pfahringer, Ricard Gavaldà

      Pages 23-37

   2. A Hierarchical Face Recognition Algorithm

      • Remco R. Bouckaert

      Pages 38-50

   3. Estimating Likelihoods for Topic Models

      • Wray Buntine

      Pages 51-64

   4. Conditional Density Estimation with Class Probability Estimators

      • Eibe Frank, Remco R. Bouckaert

      Pages 65-81

   5. Linear Time Model Selection for Mixture of Heterogeneous Components

      • Ryohei Fujimaki, Satoshi Morinaga, Michinari Momma, Kenji Aoki, Takayuki Nakata

      Pages 82-97

   6. Max-margin Multiple-Instance Learning via Semidefinite Programming

      • Yuhong Guo

      Pages 98-108

   7. A Reformulation of Support Vector Machines for General Confidence Functions

      • Yuhong Guo, Dale Schuurmans

      Pages 109-119

   8. Robust Discriminant Analysis Based on Nonparametric Maximum Entropy

      • Ran He, Bao-Gang Hu, Xiao-Tong Yuan

      Pages 120-134

   9. Context-Aware Online Commercial Intention Detection

      • Derek Hao Hu, Dou Shen, Jian-Tao Sun, Qiang Yang, Zheng Chen

      Pages 135-149

   10. Feature Selection via Maximizing Neighborhood Soft Margin

       • Qinghua Hu, Xunjian Che, Jinfu Liu

       Pages 150-161

   11. Accurate Probabilistic Error Bound for Eigenvalues of Kernel Matrix

       • Lei Jia, Shizhong Liao

       Pages 162-175

   12. Community Detection on Weighted Networks: A Variational Bayesian Method

       • Qixia Jiang, Yan Zhang, Maosong Sun

       Pages 176-190

   13. Averaged Naive Bayes Trees: A New Extension of AODE

       • Mori Kurokawa, Hiroyuki Yokoyama, Akito Sakurai

       Pages 191-205

   14. Automatic Choice of Control Measurements

       • Gayle Leen, David R. Hardoon, Samuel Kaski

       Pages 206-219

   15. Coupled Metric Learning for Face Recognition with Degraded Images

       • Bo Li, Hong Chang, Shiguang Shan, Xilin Chen

       Pages 220-233

   16. Cost-Sensitive Boosting: Fitting an Additive Asymmetric Logistic Regression Model

       • Qiu-jie Li, Yao-bin Mao, Zhi-quan Wang, Wen-bo Xiang

       Pages 234-247

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.

• Diffusion
• Support Vector Machine
• active learning
• algorithms
• artificial intelligence
• bayesian methods
• bootstrap aggregating
• classification
• data analysis
• information retrieval
• machine learning
• metric learning
• multi-agent learning
• natural language processing
• pattern maching

• National Laboratory for Novel Software Technology, Nanjing University, Nanjing, China

  Zhi-Hua Zhou

• The Institute of Scientific and Industrial Research, Osaka University, Osaka, Japan

  Takashi Washio

Policies and ethics