152,95 €*
Versandkostenfrei per Post / DHL
Lieferzeit 1-2 Wochen
Presenting a coherent structure, the book logically connects novel mathematical formulations and efficient computational solutions for a range of unsupervised learning tasks, including visual feature matching, learning and classification, object discovery, and semantic segmentation in video. The final part of the book proposes a general strategy for visual learning over several generations of student-teacher neural networks, along with a unique view on the future of unsupervised learning in real-world contexts.
Offering a fresh approach to this difficult problem, several efficient, state-of-the-art unsupervised learning algorithms are reviewed in detail, complete with an analysis of their performance on various tasks, datasets, and experimental setups. By highlighting the interconnections between these methods, many seemingly diverse problems are elegantly brought together in a unified way.
Serving as an invaluable guide to the computational tools and algorithms required to tackle the exciting challenges in the field, this book is a must-read for graduate students seeking a greater understanding of unsupervised learning, as well as researchers in computer vision, machine learning, robotics, and related disciplines.
Presenting a coherent structure, the book logically connects novel mathematical formulations and efficient computational solutions for a range of unsupervised learning tasks, including visual feature matching, learning and classification, object discovery, and semantic segmentation in video. The final part of the book proposes a general strategy for visual learning over several generations of student-teacher neural networks, along with a unique view on the future of unsupervised learning in real-world contexts.
Offering a fresh approach to this difficult problem, several efficient, state-of-the-art unsupervised learning algorithms are reviewed in detail, complete with an analysis of their performance on various tasks, datasets, and experimental setups. By highlighting the interconnections between these methods, many seemingly diverse problems are elegantly brought together in a unified way.
Serving as an invaluable guide to the computational tools and algorithms required to tackle the exciting challenges in the field, this book is a must-read for graduate students seeking a greater understanding of unsupervised learning, as well as researchers in computer vision, machine learning, robotics, and related disciplines.
Dr. Marius Leordeanu is an Associate Professor (Senior Lecturer) at the Computer Science & Engineering Department, Polytechnic University of Bucharest and a Senior Researcher at the Institute of Mathematics of the Romanian Academy (IMAR), Bucharest, Romania. In 2014, he was awarded the Grigore Moisil Prize, the most prestigious award in mathematics bestowed by the Romanian Academy, for his work on unsupervised learning.
Offers a novel approach to unsupervised learning, which connects seemingly disparate problems in the domain through unified mathematical formulations and efficient optimization algorithms
Explains, in a concise and detailed manner, how to solve specific and highly relevant tasks in computer vision and machine learning
Provides useful practical guidance and insights on unsupervised learning problems, in addition to a solid theoretical justification for each algorithm presented
| Erscheinungsjahr: | 2020 |
|---|---|
| Fachbereich: | Anwendungs-Software |
| Genre: | Informatik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Buch |
| Seiten: | 324 |
| Reihe: | Advances in Computer Vision and Pattern Recognition |
| Inhalt: |
xxiii
298 S. 136 s/w Illustr. 96 farbige Illustr. 298 p. 232 illus. 96 illus. in color. |
| ISBN-13: | 9783030421274 |
| ISBN-10: | 3030421279 |
| Sprache: | Englisch |
| Ausstattung / Beilage: | HC runder Rücken kaschiert |
| Einband: | Gebunden |
| Autor: | Leordeanu, Marius |
| Auflage: | 1st ed. 2020 |
| Hersteller: |
Springer International Publishing
Springer International Publishing AG Advances in Computer Vision and Pattern Recognition |
| Maße: | 241 x 160 x 24 mm |
| Von/Mit: | Marius Leordeanu |
| Erscheinungsdatum: | 18.04.2020 |
| Gewicht: | 0,653 kg |
Dr. Marius Leordeanu is an Associate Professor (Senior Lecturer) at the Computer Science & Engineering Department, Polytechnic University of Bucharest and a Senior Researcher at the Institute of Mathematics of the Romanian Academy (IMAR), Bucharest, Romania. In 2014, he was awarded the Grigore Moisil Prize, the most prestigious award in mathematics bestowed by the Romanian Academy, for his work on unsupervised learning.
Offers a novel approach to unsupervised learning, which connects seemingly disparate problems in the domain through unified mathematical formulations and efficient optimization algorithms
Explains, in a concise and detailed manner, how to solve specific and highly relevant tasks in computer vision and machine learning
Provides useful practical guidance and insights on unsupervised learning problems, in addition to a solid theoretical justification for each algorithm presented
| Erscheinungsjahr: | 2020 |
|---|---|
| Fachbereich: | Anwendungs-Software |
| Genre: | Informatik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Buch |
| Seiten: | 324 |
| Reihe: | Advances in Computer Vision and Pattern Recognition |
| Inhalt: |
xxiii
298 S. 136 s/w Illustr. 96 farbige Illustr. 298 p. 232 illus. 96 illus. in color. |
| ISBN-13: | 9783030421274 |
| ISBN-10: | 3030421279 |
| Sprache: | Englisch |
| Ausstattung / Beilage: | HC runder Rücken kaschiert |
| Einband: | Gebunden |
| Autor: | Leordeanu, Marius |
| Auflage: | 1st ed. 2020 |
| Hersteller: |
Springer International Publishing
Springer International Publishing AG Advances in Computer Vision and Pattern Recognition |
| Maße: | 241 x 160 x 24 mm |
| Von/Mit: | Marius Leordeanu |
| Erscheinungsdatum: | 18.04.2020 |
| Gewicht: | 0,653 kg |
Ähnliche Produkte
Lieferzeit 2-3 Wochen
Lieferzeit 4-7 Werktage
Lieferzeit 2-3 Wochen
Lieferzeit 4-7 Werktage
Lieferzeit 4-7 Werktage
Aktuell nicht verfügbar
Lieferzeit 2-3 Wochen
Lieferzeit 4-7 Werktage
Lieferzeit 2-3 Wochen
Lieferzeit 4-7 Werktage
Lieferzeit 1-2 Wochen