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ICML
2004
IEEE
15 years 10 months ago
Support vector machine learning for interdependent and structured output spaces
Learning general functional dependencies is one of the main goals in machine learning. Recent progress in kernel-based methods has focused on designing flexible and powerful input...
Ioannis Tsochantaridis, Thomas Hofmann, Thorsten J...
MICCAI
2009
Springer
15 years 4 months ago
On the Manifold Structure of the Space of Brain Images
This paper investigates an approach to model the space of brain images through a low-dimensional manifold. A data driven method to learn a manifold from a collections of brain imag...
Samuel Gerber, Tolga Tasdizen, Sarang C. Joshi, Ro...
ICTAI
1999
IEEE
15 years 2 months ago
On the Fine Structure of Large Search Spaces
Recently there has been significant progress in our understanding of the computational nature of combinatorial problems. Randomized search methods, both complete and incomplete, o...
Carla P. Gomes, Bart Selman
RTA
1997
Springer
15 years 1 months ago
Solving Linear Diophantine Equations Using the Geometric Structure of the Solution Space
In the development of algorithms for finding the minimal solutions of systems of linear Diophantine equations, little use has been made (to our knowledge) of the results by Stanle...
Ana Paula Tomás, Miguel Filgueiras
IPMI
2009
Springer
15 years 10 months ago
Marginal Space Learning for Efficient Detection of 2D/3D Anatomical Structures in Medical Images
Recently, marginal space learning (MSL) was proposed as a generic approach for automatic detection of 3D anatomical structures in many medical imaging modalities [1]. To accurately...
Yefeng Zheng, Bogdan Georgescu, Dorin Comaniciu