Special Issue on Advanced Methods in Optimization and Machine Learning for Heterogeneous Data Analytics
• 大类 : 工程技术 - 2区
• 小类 : 计算机：人工智能 - 2区
Recent advances in storage, hardware, information technology, communication, and networking have resulted in a large amount of heterogeneous data. This has powered the demand to extract useful and actionable insights from such data in an automatic, reliable and scalable way. Machine learning, which aims to construct algorithms that can learn from and make predictions on data intelligently, has attracted increasing attention in the recent years and has been successfully applied to many data analytical tasks, such as image processing, face recognition, video surveillance, document summarization, etc. Since a lot of machine learning algorithms formulate the learning tasks as linear, quadratic or semi-definite mathematical programming problems, optimization becomes a crucial tool and plays a key role in machine learning and multimedia data analysis tasks. On the other hand, machine learning and the applications in heterogeneous data analytics are not simply the consumers of optimization technology but a rapidly evolving interdisciplinary research field that is itself promoting new optimization ideas, models, and solutions.
This special issue aims to seek the high-quality papers from academics and industry-related researchers in the areas of applied mathematics, machine learning, artificial intelligence, pattern recognition, data mining, multimedia processing, and big data to show the most recently advanced methods, e.g. neural networks and learning systems, in optimization and machine learning for heterogeneous data computing.
Scope and Topics
The topics of the special issue include, but are not limited to: • Adversarial Machine Learning
• Concept Drift
• Domain Adaptation
• Distributed /Parallel Algorithms in Machine Learning