Machine Learning - Modeling Data Locally and Globally presents a novel and unified theory that tries to seamlessly integrate different algorithms. Specifically, the book distinguishes the inner nature of machine learning algorithms as either ‘local learning’or ‘global learning.’This theory not only connects previous machine learning methods, or serves as roadmap in various models, but –more importantly – it also motivates a theory that can learn from data both locally and globally. This would help the researchers gain a deeper insight andcomprehensive understanding of the techniques in this field. The book reviewscurrent topics, new theories and applications. Kaizhu Huang was a researcher at the Fujitsu Research and Development Center and is currently a research fellow in the Chinese University of Hong Kong. Haiqin Yang leads the image processing group at HiSilicon Technologies. New unified theory Detailed graphic illustration Empirical validation for each model INDICE: Introduction.- Global Learning vs. Local Learning: A Background Review.- A General Global Learning Model.- Learning Locally and Globally.- Application I: Imbalanced Learning.- Application II: Regression.- Summary.
- ISBN: 978-3-540-79451-6
- Editorial: Springer
- Encuadernacion: Cartoné
- Páginas: 300
- Fecha Publicación: 01/06/2008
- Nº Volúmenes: 1
- Idioma: Inglés