Machine Learning: Discriminative and Generative
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...
Saved in:
| Main Author: | |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Boston, MA
Springer US
2004
|
| Series: | The International Series in Engineering and Computer Science
755 |
| Subjects: | |
| Online Access: | FHI01 BTU01 Volltext Inhaltsverzeichnis |
| Summary: | 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 |
| Physical Description: | 1 Online-Ressource (XVII, 200 p) |
| ISBN: | 9781441990112 |
| DOI: | 10.1007/978-1-4419-9011-2 |
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| spelling | Jebara, Tony Verfasser aut Machine Learning Discriminative and Generative by Tony Jebara Boston, MA Springer US 2004 1 Online-Ressource (XVII, 200 p) txt rdacontent c rdamedia cr rdacarrier The International Series in Engineering and Computer Science 755 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 Computer Science Artificial Intelligence (incl. Robotics) Statistics, general Information Storage and Retrieval Computer Imaging, Vision, Pattern Recognition and Graphics Computer science Information storage and retrieval Artificial intelligence Computer graphics Statistics Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf 1\p (DE-588)4006432-3 Bibliografie gnd-content Maschinelles Lernen (DE-588)4193754-5 s 2\p DE-604 Erscheint auch als Druck-Ausgabe 9781461347569 https://doi.org/10.1007/978-1-4419-9011-2 Verlag URL des Erstveröffentlichers Volltext DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030538397&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Jebara, Tony Machine Learning Discriminative and Generative Computer Science Artificial Intelligence (incl. Robotics) Statistics, general Information Storage and Retrieval Computer Imaging, Vision, Pattern Recognition and Graphics Computer science Information storage and retrieval Artificial intelligence Computer graphics Statistics Maschinelles Lernen (DE-588)4193754-5 gnd |
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| title | Machine Learning Discriminative and Generative |
| title_auth | Machine Learning Discriminative and Generative |
| title_exact_search | Machine Learning Discriminative and Generative |
| title_full | Machine Learning Discriminative and Generative by Tony Jebara |
| title_fullStr | Machine Learning Discriminative and Generative by Tony Jebara |
| title_full_unstemmed | Machine Learning Discriminative and Generative by Tony Jebara |
| title_short | Machine Learning |
| title_sort | machine learning discriminative and generative |
| title_sub | Discriminative and Generative |
| topic | Computer Science Artificial Intelligence (incl. Robotics) Statistics, general Information Storage and Retrieval Computer Imaging, Vision, Pattern Recognition and Graphics Computer science Information storage and retrieval Artificial intelligence Computer graphics Statistics Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Computer Science Artificial Intelligence (incl. Robotics) Statistics, general Information Storage and Retrieval Computer Imaging, Vision, Pattern Recognition and Graphics Computer science Information storage and retrieval Artificial intelligence Computer graphics Statistics Maschinelles Lernen Bibliografie |
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