Pattern Recognition
Techniques and Applications
Price: 750.00 INR
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ISBN:
9780195676853
Publication date:
05/12/2005
Paperback
304 pages
242.0x185.0mm
Price: 750.00 INR
We sell our titles through other companies
Disclaimer :You will be redirected to a third party website.The sole responsibility of supplies, condition of the product, availability of stock, date of delivery, mode of payment will be as promised by the said third party only. Prices and specifications may vary from the OUP India site.
ISBN:
9780195676853
Publication date:
05/12/2005
Paperback
304 pages
242.0x185.0mm
The book begins by introducing the concept of inductive learning and provides an exhaustive coverage of the procedures for Pattern Recognition.
Suitable for: The book begins by introducing the concept of inductive learning and provides an exhaustive coverage of the procedures for Pattern Recognition. Pattern Recognition: Techniques and Applications would serve as a textbook for undergraduate and master's engineering students for the course on Pattern Recognition.
Rights: World Rights
Description
The book begins by introducing the concept of inductive learning and provides an exhaustive coverage of the procedures for Pattern Recognition.
Pattern Recognition: Techniques and Applications would serve as a textbook for undergraduate and master's engineering students for the course on Pattern Recognition. It would also be a useful reference for practicing engineers in industrial and research organizations that apply pattern recognition techniques in diverse areas such as optical character recognition, speech recognition, etc. The book begins by introducing the concept of inductive learning and provides an exhaustive coverage of the procedures for Pattern Recognition. Other relevant topics such as feature selection and clustering are also dealt with. The last chapter compares the results of classifying a given problem by each procedure, and proposes research on finding their underlying commonality, if any. The book is well supported by both programming and non-programming exercises at the end of each chapter. The appendices describe syntactic pattern recognition as well as two projects for students: one on medical prognostication and the other on optical character recognition. The lucid treatment in the book encourages self-study and instills working knowledge of pattern recognition in a student.
Table of contents
Learning to Recognize Patterns 13
Decision Trees: Basics 39
Decision Trees: Extensions 71
Obtaining Prules by Evolution 93
Bayes Classification 109
Nearest Neighbour Classification 127
Multilayer Neural Nets 143
Linear Classification 201
Cross Validation and Attribute Selection 211
Clustering 221
Syntatic Pattern Recognition 241
Summing Up 253
Features
- Exhaustive and detailed coverage of procedures of Pattern Recognition.
- Emphasis on practical applications for all the theory presented. ,li>Both programming as well as non-programming exercises for each chapter.
- Separate chapters on syntactic pattern recognition, feature selection and clustering.
- Provides projects in appendix: one on medical prognostication and the other on optical character recognition.
- Accompanied by a CD for the project on OCR
Description
The book begins by introducing the concept of inductive learning and provides an exhaustive coverage of the procedures for Pattern Recognition.
Pattern Recognition: Techniques and Applications would serve as a textbook for undergraduate and master's engineering students for the course on Pattern Recognition. It would also be a useful reference for practicing engineers in industrial and research organizations that apply pattern recognition techniques in diverse areas such as optical character recognition, speech recognition, etc. The book begins by introducing the concept of inductive learning and provides an exhaustive coverage of the procedures for Pattern Recognition. Other relevant topics such as feature selection and clustering are also dealt with. The last chapter compares the results of classifying a given problem by each procedure, and proposes research on finding their underlying commonality, if any. The book is well supported by both programming and non-programming exercises at the end of each chapter. The appendices describe syntactic pattern recognition as well as two projects for students: one on medical prognostication and the other on optical character recognition. The lucid treatment in the book encourages self-study and instills working knowledge of pattern recognition in a student.
Read MoreTable of contents
Learning to Recognize Patterns 13
Decision Trees: Basics 39
Decision Trees: Extensions 71
Obtaining Prules by Evolution 93
Bayes Classification 109
Nearest Neighbour Classification 127
Multilayer Neural Nets 143
Linear Classification 201
Cross Validation and Attribute Selection 211
Clustering 221
Syntatic Pattern Recognition 241
Summing Up 253
