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» An Introduction to Learning Structured Information
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DAGM
2008
Springer
14 years 11 months ago
Learning Visual Compound Models from Parallel Image-Text Datasets
Abstract. In this paper, we propose a new approach to learn structured visual compound models from shape-based feature descriptions. We use captioned text in order to drive the pro...
Jan Moringen, Sven Wachsmuth, Sven J. Dickinson, S...
ECTEL
2008
Springer
14 years 11 months ago
Issues in the Design of an Environment to Support the Learning of Mathematical Generalisation
Abstract. Expressing generality, recognising and analysing patterns and articulating structure is a complex task and one that is invariably problematic for students. Nonetheless, v...
Darren Pearce, Manolis Mavrikis, Eirini Geraniou, ...
COLT
2004
Springer
15 years 3 months ago
Regularization and Semi-supervised Learning on Large Graphs
We consider the problem of labeling a partially labeled graph. This setting may arise in a number of situations from survey sampling to information retrieval to pattern recognition...
Mikhail Belkin, Irina Matveeva, Partha Niyogi
NIPS
1998
14 years 11 months ago
Learning from Dyadic Data
Dyadic data refers to a domain with two nite sets of objects in which observations are made for dyads, i.e., pairs with one element from either set. This type of data arises natur...
Thomas Hofmann, Jan Puzicha, Michael I. Jordan
JMLR
2010
119views more  JMLR 2010»
14 years 4 months ago
Semi-Supervised Learning via Generalized Maximum Entropy
Various supervised inference methods can be analyzed as convex duals of the generalized maximum entropy (MaxEnt) framework. Generalized MaxEnt aims to find a distribution that max...
Ayse Erkan, Yasemin Altun