Welcome to the 9th Asian Conference on Machine Learning (ACML 2017). The conference will take place on November 15 - 17, 2017 at Baekyang Hall of Yonsei University campus, Seoul, Korea. We invite professionals and researchers to discuss research results and ideas in machine learning. We seek original and novel research papers resulting from theory and experiment of machine learning. The conference also solicits proposals focusing on disruptive ideas and paradigms within the scope. We encourage submissions from all parts of the world, not only confined to the Asia-Pacific region.
As machine plays critical role in various fields of industry, machine learning researchers needed to gather and share new ideas and achievements at a forum. ACML has begun to take place annually over the Asian regions since 2009. This is the 9th Conference to be held in Seoul, Korea after Hamilton, New Zealand (2016), Hong Kong, China (2015), Nha Trang, Vietnam (2014), Canberra, Australia (2013), Singapore (2012), Taoyuan, Taiwan (2011), Tokyo, Japan (2010), and Nanjing, China (2009). The conference has contributed to understanding the machine leaning, bringing inspiration to scientists, and applying the technologies to industries. This conference will consist of informative and integrated programs as traditions of the previous ones.
Yonsei University, one of most prestigious universities, is about 130 years old historical campus in Korea. The University street called "Sinchon" is connected to Ewha Womans University and Hongik University as one of youth hotspots. You can walk along ‘Sinchon’s Pedestrian Friendly Street’ which is full of cafes, fashion items, and beauty goods. The district is located at the heart of Seoul with easy access to cultural and attractive sites. Seoul is ranked by Asian tourists as their favorite world city three years in a row. Come experience the history and excitement of modern Seoul.
Learning problems
Active learning
Bayesian machine learning
Deep learning, latent variable models
Dimensionality reduction
Feature selection
Graphical models
Learning for big data
Learning in graphs
Multiple instance learning
Multi-objective learning
Multi-task learning
Semi-supervised learning
Sparse learning
Structured output learning
Supervised learning
Online learning
Transfer learning
Unsupervised learning
Analysis of learning systems
Computational learning theory
Experimental evaluation
Knowledge refinement
Reproducible research
Statistical learning theory
Applications
Bioinformatics
Biomedical information
Collaborative filtering
Healthcare
Computer vision
Human activity recognition
Information retrieval
Natural language processing
Social networks
Web search
Learning in knowledge-intensive systems
Knowledge refinement and theory revision
Multi-strategy learning
Other systems
11月15日
2017
11月17日
2017
初稿截稿日期
初稿录用通知日期
终稿截稿日期
注册截止日期
2016年11月16日 新西兰 Hamilton,New Zealand
第八届亚洲机器学习会议2013年11月13日 澳大利亚
第五届亚洲机器学习会议
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