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» Some new directions in graph-based semi-supervised learning
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GECCO
2004
Springer
155views Optimization» more  GECCO 2004»
15 years 5 months ago
Genetic Network Programming with Reinforcement Learning and Its Performance Evaluation
A new graph-based evolutionary algorithm named “Genetic Network Programming, GNP” has been proposed. GNP represents its solutions as directed graph structures, which can improv...
Shingo Mabu, Kotaro Hirasawa, Jinglu Hu
ICML
2007
IEEE
16 years 1 months ago
Learning random walks to rank nodes in graphs
Ranking nodes in graphs is of much recent interest. Edges, via the graph Laplacian, are used to encourage local smoothness of node scores in SVM-like formulations with generalizat...
Alekh Agarwal, Soumen Chakrabarti
90
Voted
ICML
2010
IEEE
15 years 1 months ago
Improved Local Coordinate Coding using Local Tangents
Local Coordinate Coding (LCC), introduced in (Yu et al., 2009), is a high dimensional nonlinear learning method that explicitly takes advantage of the geometric structure of the d...
Kai Yu, Tong Zhang
JIIS
2008
89views more  JIIS 2008»
15 years 10 days ago
A note on phase transitions and computational pitfalls of learning from sequences
An ever greater range of applications call for learning from sequences. Grammar induction is one prominent tool for sequence learning, it is therefore important to know its proper...
Antoine Cornuéjols, Michèle Sebag
230
Voted
DCC
2011
IEEE
14 years 6 months ago
Robust Learning of 2-D Separable Transforms for Next-Generation Video Coding
With the simplicity of its application together with compression efficiency, the Discrete Cosine Transform(DCT) plays a vital role in the development of video compression standar...
Osman Gokhan Sezer, Robert A. Cohen, Anthony Vetro