WELCOME to ICMLT 2020
Beijing, China | June 19-21, 2020
2020 5th International Conference on Machine Learning Technologies (ICMLT 2020) is going to take place at Beijing, China during June 19-21, 2020 which will offer an ideal platform for presentation, discussion, criticism, exchange of innovative ideas and current challenges in the field of machine learning technologies. It is mainly sponsored by Beijing Technology and Business University, China; organized by School of Computer and Information Engineering (BTBU), China.
2020年五届机器学习技术国际会议(ICMLT 2020)将于2020年6月19-21日在中国北京举办。本次会议将包含国际间的活动，促进不同背景，不同年龄阶段的与会者的交流，促进科研、人才的国际合作，促进机器学习领域的持续发展和科研成果转换。此次会议由北京工商大学主办, 北京工商大学计算机与信息工程学院承办, 于2020年6月19-21日在北京工商大学计算机与信息工程学院举行!
The main theme of the conference is to address and deliberate on the latest technical status and recent trends in the research and applications of machine learning technologies. The purpose of the conference is to provide an opportunity for the scientists, engineers, industrialists, scholars and other professionals from all over the world to interact and exchange their new ideas and research outcomes in related fields and develop possible chances for future collaboration. The conference is also aimed at motivating the next generation of researchers to promote their interests in machine learning technologies.
Machine Learning usually plays an important role in the transition from data storage to decision systems based on large databases of signals such as the obtained from sensor networks, internet services, or communication systems. These systems imply developing both computational solutions and novel models. Signals from real-world systems are usually complex such as speech, music, bio-medical, multimedia, among others. Thus, Signal Processing techniques are very useful for these type of systems to automate processing and analysis techniques to retrieve information from data storage. Topics will range from foundations for real-world systems, and processing, such as speech, language analysis, biomedicine, convergence and complexity analysis, machine learning, social networks, sparse representations, visual analytics, robust statistical methods.