Librería Portfolio

This book adopts a detailed and methodological algorithmic approach to explain the concepts of pattern recognition. While the text provides a systematic account of its major topics such as pattern representation and nearest neighbour based classifiers, current topics - neural networks, support vector machines and decision trees - attributed to the recent vast progress in this field are also dealt with. Introduction to Pattern Recognition and Machine Learning will equip readers, especially senior computer science undergraduates, with a deeper understanding of the subject matter.



Contents:
Introduction
Types of Data
Feature Extraction and Feature Selection
Bayesian Learning
Classification
Classification Using Soft Computing Techniques
Data Clustering
Soft Clustering
Application - Social and Information Networks