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Subspace, Latent Structure and Feature Selection : Statistical and Optimization Perspectives Workshop, SLSFS 2005 Bohinj, Slovenia, February 23-25, 2005, Revised Selected Papers / edited by Craig Saunders, Marko Grobelnik, Steve Gunn, John Shawe-Taylor.
SpringerLink Books Lecture Notes In Computer Science (LNCS) (1997-2024) Available online
View online- Format:
- Book
- Series:
- Computer Science (Springer-11645)
- LNCS sublibrary. Theoretical computer science and general issues ; SL 1, 3940.
- Theoretical Computer Science and General Issues ; 3940
- Language:
- English
- Subjects (All):
- Algorithms.
- Mathematical statistics.
- Computers.
- Artificial intelligence.
- Optical data processing.
- Pattern perception.
- Algorithm Analysis and Problem Complexity.
- Probability and Statistics in Computer Science.
- Computation by Abstract Devices.
- Artificial Intelligence.
- Image Processing and Computer Vision.
- Pattern Recognition.
- Local Subjects:
- Algorithm Analysis and Problem Complexity.
- Probability and Statistics in Computer Science.
- Computation by Abstract Devices.
- Artificial Intelligence.
- Image Processing and Computer Vision.
- Pattern Recognition.
- Physical Description:
- 1 online resource (X, 209 pages).
- Edition:
- First edition 2006.
- Contained In:
- Springer eBooks
- Place of Publication:
- Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2006.
- System Details:
- text file PDF
- Contents:
- Invited Contributions
- Discrete Component Analysis
- Overview and Recent Advances in Partial Least Squares
- Random Projection, Margins, Kernels, and Feature-Selection
- Some Aspects of Latent Structure Analysis
- Feature Selection for Dimensionality Reduction
- Contributed Papers
- Auxiliary Variational Information Maximization for Dimensionality Reduction
- Constructing Visual Models with a Latent Space Approach
- Is Feature Selection Still Necessary?
- Class-Specific Subspace Discriminant Analysis for High-Dimensional Data
- Incorporating Constraints and Prior Knowledge into Factorization Algorithms - An Application to 3D Recovery
- A Simple Feature Extraction for High Dimensional Image Representations
- Identifying Feature Relevance Using a Random Forest
- Generalization Bounds for Subspace Selection and Hyperbolic PCA
- Less Biased Measurement of Feature Selection Benefits.
- Other Format:
- Printed edition:
- ISBN:
- 978-3-540-34138-3
- 9783540341383
- Access Restriction:
- Restricted for use by site license.
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