Ensemble Learning Reading List — Data Community DCSkip to search form Skip to main content. UCS Hybrid and ensemble methods in machine learning have attracted a great attention of the scientific community over the last years [Zhou, 12]. Multiple, ensemble learning models have been theoretically and empirically shown to provide significantly better performance than single weak learners, especially while dealing with high dimensional, complex regression and classification problems [Brazdil, 09], [Okun, 08]. View PDF. Save to Library. Create Alert. Share This Paper.
Ensemble Methods: Foundations and Algorithms
Ensemble methods : foundations and algorithms Author. Zhou, Zhi-Hua Ph. Physical description. Description based upon print version of record. Includes bibliographical references and index. Reproduction available: Electronic reproduction.
Welcome to CRCPress. Please choose www. Your GarlandScience. The student resources previously accessed via GarlandScience. Resources to the following titles can be found at www. What are VitalSource eBooks? For Instructors Request Inspection Copy.
The manuscripts should be submitted in PDF format. Click Here to know further guidelines for submission. Ensemble learning attempts to enhance the performance of systems clustering, classification, prediction, feature selection, search, optimization, rule extraction, etc. This approach is intuitively meaningful as a single model may not always be the best for solving a complex problem also known as the no free lunch theorem while multiple models are more likely to yield results better than each of the constituent models. Although in the past, ensemble methods have been mainly studied in the context of classification and time series prediction, recently they are being used in algorithms in other scenarios such as clustering, fuzzy systems, evolutionary algorithms, dimensionality reduction and so on. The aim of this symposium is to bring together researchers and practitioners who are working in the overlapping fields of ensemble methods and computational intelligence. Papers dealing with theory, algorithms, analysis, and applications of ensemble of computational intelligence methods are sought for this symposium.