On neurobiological, neuro-fuzzy, machine learning, and statistical pattern recognition techniques

On neurobiological, neuro-fuzzy, machine learning, and statistical pattern recognition techniques

In this paper, we propose two new neuro-fuzzy schemes, one for classification and one for clustering problems. The classification scheme is based on Simpson's fuzzy min-max method (1992, 1993) and relaxes some assumptions he makes. This enables our scheme to handle mutually nonexclusive classes. The neuro-fuzzy clustering scheme is a multiresolution algorithm that is modeled after the mechanics...

Machine Learning Pattern Recognition

Introduction These lecture notes were written to provide you with a handy reference to the material that was presented in the Machine Learning: Pattern Recognition course. It is not meant to replace the book of the course [Bishop, 2007], rather, it is meant to illustrate topics that may seem a bit dry or emphasise subjects that are deemed important to this class. It will often repeat what was s...

Machine Learning and Pattern Recognition

Pattern Recognition and Machine Learning

his book provides an introduction to the eld of pattern recognition and machine earning. It gives an overview of several asic and advanced topics in machine earning theory. The book is definitely aluable to scientists and engineers who re involved in developing machine learnng tools applied to signal and image proessing applications. This book is also uitable for courses on machine learning nd ...

Pattern Recognition and Machine Learning

In this note, we describe the sequential minimal optimization (SMO) algorithm to solve the soft margin support vector machine (SVM) binary classification problem, which is to be implemented as part of the miniproject. In order to understand our arguments here, the reader is advised to study the SVM chapter of the course notes, in particular the “Solving the Support Vector Machine” section (the ...