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» Structured metric learning for high dimensional problems
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ICPR
2006
IEEE
14 years 8 months ago
A New Data Selection Principle for Semi-Supervised Incremental Learning
Current semi-supervised incremental learning approaches select unlabeled examples with predicted high confidence for model re-training. We show that for many applications this dat...
Alexander I. Rudnicky, Rong Zhang
PAMI
2010
417views more  PAMI 2010»
13 years 6 months ago
Auto-Context and Its Application to High-Level Vision Tasks and 3D Brain Image Segmentation
The notion of using context information for solving high-level vision and medical image segmentation problems has been increasingly realized in the field. However, how to learn a...
Zhuowen Tu, Xiang Bai
EDBT
2006
ACM
145views Database» more  EDBT 2006»
14 years 7 months ago
Indexing Spatially Sensitive Distance Measures Using Multi-resolution Lower Bounds
Abstract. Comparison of images requires a distance metric that is sensitive to the spatial location of objects and features. Such sensitive distance measures can, however, be compu...
Vebjorn Ljosa, Arnab Bhattacharya, Ambuj K. Singh
NIPS
2001
13 years 9 months ago
Convolution Kernels for Natural Language
We describe the application of kernel methods to Natural Language Processing (NLP) problems. In many NLP tasks the objects being modeled are strings, trees, graphs or other discre...
Michael Collins, Nigel Duffy
TON
2008
126views more  TON 2008»
13 years 7 months ago
Hyperbolic embedding of internet graph for distance estimation and overlay construction
Estimating distances in the Internet has been studied in the recent years due to its ability to improve the performance of many applications, e.g., in the peer-topeer realm. One sc...
Yuval Shavitt, Tomer Tankel