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ICML
2003
IEEE
16 years 7 months ago
Learning Metrics via Discriminant Kernels and Multidimensional Scaling: Toward Expected Euclidean Representation
Distance-based methods in machine learning and pattern recognition have to rely on a metric distance between points in the input space. Instead of specifying a metric a priori, we...
Zhihua Zhang
ICPR
2006
IEEE
16 years 7 months ago
Radon space and Adaboost for Pose Estimation
In this paper, we present a new approach to camera pose estimation from single shot images in known environment. Such a method comprises two stages, a learning step and an inferen...
Patrick Etyngier, Nikos Paragios, Renaud Keriven, ...
CHI
2009
ACM
16 years 1 months ago
CThru: exploration in a video-centered information space for educational purposes
We present CThru, a self-guided video-based educational environment in a large multi-display setting. We employ a video-centered approach, creating and combining multimedia conten...
Hao Jiang, Alain Viel, Meekal Bajaj, Robert A. Lue...
CIBCB
2005
IEEE
16 years 7 days ago
The Homology Kernel: A Biologically Motivated Sequence Embedding into Euclidean Space
— Part of the challenge of modeling protein sequences is their discrete nature. Many of the most powerful statistical and learning techniques are applicable to points in a Euclid...
Eleazar Eskin, Sagi Snir
NAACL
2007
15 years 8 months ago
A Systematic Exploration of the Feature Space for Relation Extraction
Relation extraction is the task of finding semantic relations between entities from text. The state-of-the-art methods for relation extraction are mostly based on statistical lea...
Jing Jiang, ChengXiang Zhai