Luh Yen

According to our database1, Luh Yen authored at least 13 papers between 2004 and 2012.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

On csauthors.net:

Bibliography

2012
An experimental investigation of kernels on graphs for collaborative recommendation and semisupervised classification.
Neural Networks, 2012

2011
A Link Analysis Extension of Correspondence Analysis for Mining Relational Databases.
IEEE Trans. Knowl. Data Eng., 2011

2010
Proximities on graphs : application to node clustering and visualization.
PhD thesis, 2010

The Sum-over-Paths Covariance Kernel: A Novel Covariance Measure between Nodes of a Directed Graph.
IEEE Trans. Pattern Anal. Mach. Intell., 2010

2009
Randomized Shortest-Path Problems: Two Related Models.
Neural Comput., 2009

Graph nodes clustering with the sigmoid commute-time kernel: A comparative study.
Data Knowl. Eng., 2009

2008
Tuning continual exploration in reinforcement learning: An optimality property of the Boltzmann strategy.
Neurocomputing, 2008

A family of dissimilarity measures between nodes generalizing both the shortest-path and the commute-time distances.
Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2008

2007
Graph Nodes Clustering Based on the Commute-Time Kernel.
Proceedings of the Advances in Knowledge Discovery and Data Mining, 2007

2006
An Experimental Investigation of Graph Kernels on a Collaborative Recommendation Task.
Proceedings of the 6th IEEE International Conference on Data Mining (ICDM 2006), 2006

Optimal Tuning of Continual Online Exploration in Reinforcement Learning.
Proceedings of the Artificial Neural Networks, 2006

2005
clustering using a random walk based distance measure.
Proceedings of the 13th European Symposium on Artificial Neural Networks, 2005

2004
The Principal Components Analysis of a Graph, and Its Relationships to Spectral Clustering.
Proceedings of the Machine Learning: ECML 2004, 2004


  Loading...