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» A Framework for Multiple-Instance Learning
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ECCV
2008
Springer
14 years 11 months ago
Discriminative Learning for Deformable Shape Segmentation: A Comparative Study
Abstract. We present a comparative study on how to use discriminative learning methods such as classification, regression, and ranking to address deformable shape segmentation. Tra...
Jingdan Zhang, Shaohua Kevin Zhou, Dorin Comaniciu...
JMLR
2012
11 years 11 months ago
Distance Metric Learning with Eigenvalue Optimization
The main theme of this paper is to develop a novel eigenvalue optimization framework for learning a Mahalanobis metric. Within this context, we introduce a novel metric learning a...
Yiming Ying, Peng Li
JMLR
2010
121views more  JMLR 2010»
13 years 4 months ago
Sparse Semi-supervised Learning Using Conjugate Functions
In this paper, we propose a general framework for sparse semi-supervised learning, which concerns using a small portion of unlabeled data and a few labeled data to represent targe...
Shiliang Sun, John Shawe-Taylor
KDD
2001
ACM
166views Data Mining» more  KDD 2001»
14 years 9 months ago
Generalized clustering, supervised learning, and data assignment
Clustering algorithms have become increasingly important in handling and analyzing data. Considerable work has been done in devising effective but increasingly specific clustering...
Annaka Kalton, Pat Langley, Kiri Wagstaff, Jungsoo...
ICCV
2007
IEEE
14 years 3 months ago
Shape Priors using Manifold Learning Techniques
We introduce a non-linear shape prior for the deformable model framework that we learn from a set of shape samples using recent manifold learning techniques. We model a category o...
Patrick Etyngier, Florent Ségonne, Renaud K...