Regularization and learning theory

Regularization theory mainly used in the branch of mathematics and in particular in the fields of machine learning and inverse problems. This concept used in order to solve an ill-posed inverse problem or to prevent overfitting. This information is usually of the form of a penalty for complexity such as restrictions for smoothness or bounds on the vector space norm. Conversion of machine learning problems to ill-posed inverse and how we can apply these techniques in real life problem should be learned. This books gives little idea to do the above job.