You might also like

More about Machine Learning

• Book details
• Synopsis
• Trending among others

You get 249 loyalty points

Book synopsis

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. §

Book details

Book category Books in English Computing & information technology Computer science Artificial intelligence

• Full title: Machine Learning
• Subtitle: Discriminative and Generative
• Author: Tony Jebara
• Language: English
• Binding: Paperback
• Number of pages: 200
• EAN: 9781461347569
• ISBN: 1461347564
• ID: 02016271
• Publisher: Springer-Verlag New York Inc.
• Weight: 349 g
• Dimensions: 235 × 155 × 13 mm
• Date of publishing: 27. September 2012

Trending among others

---

---

---