Ensamble Learning: An Approach in Artificial Intelligence

Abstract

Decades ago, in the field of machine learning and data mining, the development of methods of ensemble learning has received significant attention from science community. Machine integration techniques incorporate multiple learning acquisition skills, better performance of guesswork than you would find in any available learning skills alone. Combining multiple learning models is demonstrated in thought and experimentation providing better performance than single foundation students. In a book, mix learning algorithms form a dominant and high-level approach to high throughput performance, thus applied to real-world problems ranging from face to face emotional recognition through classification and medical diagnosis in financial forecasting.