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DAGM
2004
Springer
14 years 1 months ago
Learning from Labeled and Unlabeled Data Using Random Walks
We consider the general problem of learning from labeled and unlabeled data. Given a set of points, some of them are labeled, and the remaining points are unlabeled. The goal is to...
Dengyong Zhou, Bernhard Schölkopf
STOC
2002
ACM
99views Algorithms» more  STOC 2002»
14 years 8 months ago
Recognizing string graphs in NP
A string graph is the intersection graph of a set of curves in the plane. Each curve is represented by a vertex, and an edge between two vertices means that the corresponding curv...
Marcus Schaefer, Eric Sedgwick, Daniel Stefankovic
CVPR
2007
IEEE
14 years 9 months ago
Image Classification with Segmentation Graph Kernels
We propose a family of kernels between images, defined as kernels between their respective segmentation graphs. The kernels are based on soft matching of subtree-patterns of the r...
Francis Bach, Zaïd Harchaoui
CVPR
2008
IEEE
14 years 9 months ago
Unsupervised learning of probabilistic object models (POMs) for object classification, segmentation and recognition
We present a new unsupervised method to learn unified probabilistic object models (POMs) which can be applied to classification, segmentation, and recognition. We formulate this a...
Yuanhao Chen, Long Zhu, Alan L. Yuille, HongJiang ...
MLG
2007
Springer
14 years 1 months ago
Transductive Rademacher Complexities for Learning Over a Graph
Recent investigations [12, 2, 8, 5, 6] and [11, 9] indicate the use of a probabilistic (’learning’) perspective of tasks defined on a single graph, as opposed to the traditio...
Kristiaan Pelckmans, Johan A. K. Suykens