Partially Supervised Learning First IAPR TC3 Workshop, PSL 2011, Ulm, Germany, September 15-16, 2011, Revised Selected Papers / [electronic resource] :
edited by Friedhelm Schwenker, Edmondo Trentin.
- 1st ed. 2012.
- X, 159 p. online resource.
- Lecture Notes in Artificial Intelligence, 7081 2945-9141 ; .
- Lecture Notes in Artificial Intelligence, 7081 .
This book constitutes thoroughly refereed revised selected papers from the First IAPR TC3 Workshop on Partially Supervised Learning, PSL 2011, held in Ulm, Germany, in September 2011. The 14 papers presented in this volume were carefully reviewed and selected for inclusion in the book, which also includes 3 invited talks. PSL 2011 dealt with methodological issues as well as real-world applications of PSL. The main methodological issues were: combination of supervised and unsupervised learning; diffusion learning; semi-supervised classification, regression, and clustering; learning with deep architectures; active learning; PSL with vague, fuzzy, or uncertain teaching signals; learning, or statistical pattern recognition; and PSL in cognitive systems. Applications of PSL included: image and signal processing; multi-modal information processing; sensor/information fusion; human computer interaction; data mining and Web mining; forensic anthropology; and bioinformatics.
9783642282584
10.1007/978-3-642-28258-4 doi
Artificial intelligence.
Pattern recognition systems.
Computer vision.
Application software.
Bioinformatics.
Algorithms.
Artificial Intelligence.
Automated Pattern Recognition.
Computer Vision.
Computer and Information Systems Applications.
Computational and Systems Biology.
Algorithms.
Q334-342 TA347.A78
006.3
This book constitutes thoroughly refereed revised selected papers from the First IAPR TC3 Workshop on Partially Supervised Learning, PSL 2011, held in Ulm, Germany, in September 2011. The 14 papers presented in this volume were carefully reviewed and selected for inclusion in the book, which also includes 3 invited talks. PSL 2011 dealt with methodological issues as well as real-world applications of PSL. The main methodological issues were: combination of supervised and unsupervised learning; diffusion learning; semi-supervised classification, regression, and clustering; learning with deep architectures; active learning; PSL with vague, fuzzy, or uncertain teaching signals; learning, or statistical pattern recognition; and PSL in cognitive systems. Applications of PSL included: image and signal processing; multi-modal information processing; sensor/information fusion; human computer interaction; data mining and Web mining; forensic anthropology; and bioinformatics.
9783642282584
10.1007/978-3-642-28258-4 doi
Artificial intelligence.
Pattern recognition systems.
Computer vision.
Application software.
Bioinformatics.
Algorithms.
Artificial Intelligence.
Automated Pattern Recognition.
Computer Vision.
Computer and Information Systems Applications.
Computational and Systems Biology.
Algorithms.
Q334-342 TA347.A78
006.3