From the introduction of a manuscript that I recently submitted for review
The practice of Machine Learning research can be characterized as the effective semiprincipled reduction of learning problems to problems for which robust and efficient solution techniques exist – ideally ones with provable bounds on their use of time and space. In a recent paper Bennett and Parrado-Hern´andez (2006) describe the synergistic relationship between the fields of machine learning (ML) and mathematical programming (MP). They remark:
“Optimization lies at the heart of machine learning. Most machine learning problems reduce to optimization problems. Consider the machine learning analyst in action solving a problem for some set of data. The modeler formulates the problem by selecting an appropriate family of models and massages the data into a format amenable to modeling. Then the model is typically trained by solving a core optimization problem that Continue reading “Optimization, Adaptation, Machine Learning and Evolutionary Computation”
evorithmics 