Machine Learning Approaches to Learning Heuristics for Combinatorial Optimization Problems

Generic Heuristics for Combinatorial Optimization Problems

This paper discusses the use of several heuristic techniques for problems of combinatorial optimization. We especially consider the advantages and disadvantages of naturally inspired generic techniques like Simulated Annealing, Evolution Strategies, or Genetic Algorithms. This reflection gives a quite intuitive motivation for hybrid approaches that aim to combine advantageous aspects of the cer...

Statistical Machine Learning and Combinatorial Optimization

Approaches to machine learning

The field of machine learning strives to develop methods and techniques to automate the acquisition of new information, new skills, and new ways of organizing existing information. In this article, we review the major approaches to machine learning in symbolic domains, covering the tasks of learning concepts from examples, learning search methods, conceptual clustering, and language acquisition...

Machine learning problems from optimization perspective

Both optimization and learning play important roles in a system for intelligent tasks. On one hand, we introduce three types of optimization tasks studied in the machine learning literature, corresponding to the three levels of inverse problems in an intelligent system. Also, we discuss three major roles of convexity in machine learning, either directly towards a convex programming or approxima...

Machine Learning Approaches to Bioinformatics

The book succeeds on two key unique features. First, it introduces the most widely used machine learning approaches in bioinformatics and discusses, with evaluations from real case studies, how they are used in individual bioinformatics projects. Second, it introduces state-of-the-art bioinformatics research methods. The theoretical parts and the practical parts are well integrated for readers ...