Computational Methods and Application in Machine Learning, 2nd Edition

Dear Colleagues,

Machine learning is an interdisciplinary subject involving probability theory, statistics, approximation theory, convex analysis, optimalization, algorithm complexity theory, etc. It focuses on how computers simulate or realize human learning behaviors, so as to obtain new knowledge or skills. It is the core of artificial intelligence. In essence, the aim of machine learning is to enable computers to simulate human learning behaviors, automatically acquire knowledge and skills through learning, continuously improve performance, and realize artificial intelligence.

The main focus of this Special Issue is the progress of machine learning methods and applications, as well as emerging intelligent applications and models in topics of interest, including, but not limited to, information retrieval, expert systems, automatic reasoning, natural language understanding, pattern recognition, computer vision, intelligent robot, and deep learning.

The goal of this Special Issue is to establish a community of authors and readers to discuss the latest research, propose new ideas and research directions, and associate them with practical applications. In terms of application, we welcome papers including, but not limited to, the following topics: new machine learning models for vision, natural language, bioinformatics, intelligent robots, and expert systems. We will consider any theoretically solid contributions to the fields related to machine learning.

Prof. Dr. Huawen Liu
Dr. Chengyuan Zhang
Dr. Chunwei Tian
Guest Editors

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