Optimization of Classifiers using Genetic Programming
Developing Optimal Composite Classifiers using Genetic Programming for Pattern Classification problems
Versandkostenfrei!
Versandfertig in 6-10 Tagen
42,99 €
inkl. MwSt.
PAYBACK Punkte
21 °P sammeln!
The material in the book is useful for the beginners, graduate students and teachers working in the fields of pattern recognition, image processing, machine learning, and computational intelligence. This book is also fruitful for scientists, researchers, and engineers who want to develop their improved performance classification models for pattern recognition / classification problems. This book focuses the development of various classification models using genetic programming (GP) optimization. This technique is employed in various stages of the pattern classification. The success of classifi...
The material in the book is useful for the beginners, graduate students and teachers working in the fields of pattern recognition, image processing, machine learning, and computational intelligence. This book is also fruitful for scientists, researchers, and engineers who want to develop their improved performance classification models for pattern recognition / classification problems. This book focuses the development of various classification models using genetic programming (GP) optimization. This technique is employed in various stages of the pattern classification. The success of classification system highly depends on the improvement of its classification stage. The book has investigated the potential of genetic programming search space to optimize the performance of various machine-learning approaches including linear, support vector machines, statistical, and nearest neighbor. The main advantage of GP technique is that,during training, it automatically selection suitable component classifiers for optimal combination. In the book, the improved performance of composite classifiers is evaluated for various pattern classification problems.
Andere Kunden interessierten sich für
