Buch, Englisch, 289 Seiten, Previously published in hardcover, Format (B × H): 155 mm x 235 mm, Gewicht: 474 g
Reihe: Cognitive Technologies
Case Studies on Organization and Retrieval
Buch, Englisch, 289 Seiten, Previously published in hardcover, Format (B × H): 155 mm x 235 mm, Gewicht: 474 g
Reihe: Cognitive Technologies
ISBN: 978-3-642-44362-6
Verlag: Springer
Processing multimedia content has emerged as a key area for the application of machine learning techniques, where the objectives are to provide insight into the domain from which the data is drawn, and to organize that data and improve the performance of the processes manipulating it. Arising from the EU MUSCLE network, this multidisciplinary book provides a comprehensive coverage of the most important machine learning techniques used and their application in this domain.
Zielgruppe
Professional/practitioner
Autoren/Hrsg.
Fachgebiete
- Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik Robotik
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme
- Mathematik | Informatik EDV | Informatik Daten / Datenbanken Datenkompression, Dokumentaustauschformate
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
- Mathematik | Informatik EDV | Informatik Informatik Mensch-Maschine-Interaktion Informationsvisualisierung
- Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Grafikprogrammierung
Weitere Infos & Material
to Learning Principles for Multimedia Data.- to Bayesian Methods and Decision Theory.- Supervised Learning.- Unsupervised Learning and Clustering.- Dimension Reduction.- Multimedia Applications.- Online Content-Based Image Retrieval Using Active Learning.- Conservative Learning for Object Detectors.- Machine Learning Techniques for Face Analysis.- Mental Search in Image Databases: Implicit Versus Explicit Content Query.- Combining Textual and Visual Information for Semantic Labeling of Images and Videos.- Machine Learning for Semi-structured Multimedia Documents: Application to Pornographic Filtering and Thematic Categorization.- Classification and Clustering of Music for Novel Music Access Applications.
