Designing a Boosted Classifier on Riemannian Manifolds
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Publication:2807060
DOI10.1007/978-3-319-22957-7_13zbMath1359.94040OpenAlexW2274922593MaRDI QIDQ2807060
Peter Meer, Oncel Tuzel, Fatih Porikli
Publication date: 19 May 2016
Published in: Riemannian Computing in Computer Vision (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/978-3-319-22957-7_13
Directional data; spatial statistics (62H11) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08) General geometric structures on manifolds (almost complex, almost product structures, etc.) (53C15)
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Cites Work
- Riemannian geometry for the statistical analysis of diffusion tensor data
- Additive logistic regression: a statistical view of boosting. (With discussion and a rejoinder by the authors)
- A trainable system for object detection
- Support-vector networks
- How to conjugate C\(^1\)-close group actions
- A Riemannian framework for tensor computing
- The Efficiency of Logistic Regression Compared to Normal Discriminant Analysis
- A Differential Geometric Approach to the Geometric Mean of Symmetric Positive-Definite Matrices