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103
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NIPS
2000
15 years 2 months ago
Structure Learning in Human Causal Induction
We use graphical models to explore the question of how people learn simple causal relationships from data. The two leading psychological theories can both be seen as estimating th...
Joshua B. Tenenbaum, Thomas L. Griffiths
95
Voted
ICPR
2004
IEEE
16 years 1 months ago
Discrimination of Machine-Printed from Handwritten Text Using Simple Structural Characteristics
In this paper, we present a trainable approach to discriminate between machine-printed and handwritten text. An integrated system able to localize text areas and split them in tex...
Efstathios Stamatatos, Ergina Kavallieratou
WSOM
2009
Springer
15 years 7 months ago
Towards Semi-supervised Manifold Learning: UKR with Structural Hints
We explore generic mechanisms to introduce structural hints into the method of Unsupervised Kernel Regression (UKR) in order to learn representations of data sequences in a semi-su...
Jan Steffen, Stefan Klanke, Sethu Vijayakumar, Hel...
77
Voted
GMP
2006
IEEE
102views Solid Modeling» more  GMP 2006»
15 years 6 months ago
Representing Topological Structures Using Cell-Chains
Abstract. A new topological representation of surfaces in higher dimensions, “cell-chains” is developed. The representation is a generalization of Brisson’s cell-tuple data s...
David E. Cardoze, Gary L. Miller, Todd Phillips
ICDM
2005
IEEE
134views Data Mining» more  ICDM 2005»
15 years 6 months ago
A Preference Model for Structured Supervised Learning Tasks
The preference model introduced in this paper gives a natural framework and a principled solution for a broad class of supervised learning problems with structured predictions, su...
Fabio Aiolli