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» Graph Based Semi-supervised Learning with Sharper Edges
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144
Voted
SAT
2009
Springer
117views Hardware» more  SAT 2009»
15 years 10 months ago
Dynamic Symmetry Breaking by Simulating Zykov Contraction
Abstract. We present a new method to break symmetry in graph coloring problems. While most alternative techniques add symmetry breaking predicates in a pre-processing step, we deve...
Bas Schaafsma, Marijn Heule, Hans van Maaren
187
Voted
PRIB
2010
Springer
192views Bioinformatics» more  PRIB 2010»
15 years 1 months ago
Structured Output Prediction of Anti-cancer Drug Activity
We present a structured output prediction approach for classifying potential anti-cancer drugs. Our QSAR model takes as input a description of a molecule and predicts the activity...
Hongyu Su, Markus Heinonen, Juho Rousu
FCT
2009
Springer
15 years 10 months ago
Competitive Group Testing and Learning Hidden Vertex Covers with Minimum Adaptivity
Suppose that we are given a set of n elements d of which are “defective”. A group test can check for any subset, called a pool, whether it contains a defective. It is well know...
Peter Damaschke, Azam Sheikh Muhammad
KDD
2008
ACM
150views Data Mining» more  KDD 2008»
16 years 3 months ago
Hypergraph spectral learning for multi-label classification
A hypergraph is a generalization of the traditional graph in which the edges are arbitrary non-empty subsets of the vertex set. It has been applied successfully to capture highord...
Liang Sun, Shuiwang Ji, Jieping Ye
ECCV
2010
Springer
15 years 8 months ago
From a Set of Shapes to Object Discovery
Abstract. This paper presents an approach to object discovery in a given unlabeled image set, based on mining repetitive spatial configurations of image contours. Contours that si...