The book "Novel Fisher Subspace based Classifiers" offers a comprehensive introduction to novel classifiers based on an innovative approach to estimating the Fisher subspace. It addresses various classification tasks and demonstrates the quality of these methods. The classifiers presented are designed to tackle specific challenges in data classification, including handling unbalanced classes and addressing the issue of small sample sizes. Furthermore, it shows how these classifiers manage high-dimensional data and perform classification tasks where the dimension of the space is nearly equal to the number of training data points. Another goal is to relax the constraint of linear separability to reduce overfitting issues. This specialized book is aimed at professionals and students in the fields of engineering and IT who wish to engage with modern classification methods.