Abstract
Statistical learning is a subject that integrates statistics, optimization and functional analysis techniques for using data to predict the future. This subject can be applied in many fields such as industrial engineering and computer science. Linear programming (LP) is a mathematical program design with linear objective function subject to linear equality and inequality constraints. However, studies rarely estimate the LP parameter. We develop a learning machine, namely, LP machine, based on the framework of statistical learning to estimate the unknown LP parameter. We verify the existence of a solution, and derive the Karush-Kuhn-Tucker condition. Furthermore, we incorporate the concept of complexity into the LP machine and investigate its learning bound.