Buch, Englisch, Band 755, 200 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 347 g
Reihe: The Springer International Series in Engineering and Computer Science
Discriminative and Generative
Buch, Englisch, Band 755, 200 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 347 g
Reihe: The Springer International Series in Engineering and Computer Science
ISBN: 978-1-4613-4756-9
Verlag: Springer US
Machine Learning:Discriminative and Generative covers the main contemporary themes and tools in machine learning ranging from Bayesian probabilistic models to discriminative support-vector machines. However, unlike previous books that only discuss these rather different approaches in isolation, it bridges the two schools of thought together within a common framework, elegantly connecting their various theories and making one common big-picture. Also, this bridge brings forth new hybrid discriminative-generative tools that combine the strengths of both camps. This book serves multiple purposes as well. The framework acts as a scientific breakthrough, fusing the areas of generative and discriminative learning and will be of interest to many researchers. However, as a conceptual breakthrough, this common framework unifies many previously unrelated tools and techniques and makes them understandable to a larger portion of the public. This gives the more practical-minded engineer, student and the industrial public an easy-access and more sensible road map into the world of machine learning.
Machine Learning: Discriminative and Generative is designed for an audience composed of researchers & practitioners in industry and academia. The book is also suitable as a secondary text for graduate-level students in computer science and engineering.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Warehouse
- Mathematik | Informatik EDV | Informatik Daten / Datenbanken Information Retrieval
- Mathematik | Informatik EDV | Informatik Informatik Bildsignalverarbeitung
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
- Mathematik | Informatik Mathematik Stochastik
Weitere Infos & Material
1. Introduction.- 2. Generative Versus Discriminative Learning.- 3. Maximum Entropy Discrimination.- 4. Extensions to Med.- 5. Latent Discrimination.- 6. Conclusion.- 7. Appendix.
