Introduction to Pattern Recognition and Machine Learning
Nonfiction, Computers, Advanced Computing, Engineering, Computer Vision, Computer Science, General Computing
| Author: | M Narasimha Murty, V Susheela Devi | ISBN: | 9789814656276 |
| Publisher: | World Scientific Publishing Company | Publication: | April 22, 2015 |
| Imprint: | WSPC | Language: | English |
| Author: | M Narasimha Murty, V Susheela Devi |
| ISBN: | 9789814656276 |
| Publisher: | World Scientific Publishing Company |
| Publication: | April 22, 2015 |
| Imprint: | WSPC |
| Language: | English |
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
Readership: Academics and working professionals in computer science.
Key Features:
- The algorithmic approach taken and the practical issues dealt with will aid the reader in writing programs and implementing methods
- Covers recent and advanced topics by providing working exercises, examples and illustrations in each chapter
- Provides the reader with a deeper understanding of the subject matter
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
Readership: Academics and working professionals in computer science.
Key Features:
- The algorithmic approach taken and the practical issues dealt with will aid the reader in writing programs and implementing methods
- Covers recent and advanced topics by providing working exercises, examples and illustrations in each chapter
- Provides the reader with a deeper understanding of the subject matter
