Minimum error entropy classification. (Q2429148)

The paper deals with the theoretical background and corresponding applications of minimum error entropy (MEE) to different data classifications models, such as: multilayer perceptron, recurrent neural networks, and clustering algorithms. Many examples and tests are also provided to illustrate the practical application of MEE in concrete classification problems. The book is dedicated to researchers and practitioners working on machine learning algorithms interested in using MEE in data classification.