MODeLING.Vis: A Graphical User Interface Toolbox Developed for Machine Learning and Pattern Recognition of Biomolecular Data

Machine learning approches and pattern recognition for spectral data

The adaptive and automated analysis of spectral data plays an important role in many areas of research such as physics, astronomy and geophysics, chemistry, bioinformatics, biochemistry, engineering, and others. The amount of data may range from several billion samples in geophysics to only a few in medical applications. Further, a vectorial representation of spectra typically leads to huge-dim...

Graphical-Based Learning Environments for Pattern Recognition

In this paper, we present a new neural network model, called graph neural network model, which is a generalization of two existing approaches, viz., the graph focused approach, and the node focused approach. The graph focused approach considers the mapping from a graph structure to a real vector, in which the mapping is independent of the particular node involved; while the node focused approac...

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 ...