Name
Title | ||
|---|---|---|
A Maximum-Likelihood Connectionist Model for Unsupervised Learning over Graphical Domains |
Abstract | ||
|---|---|---|
Supervised relational learning over labeled graphs, e.g. via recursive neural nets, received considerable attention from the connectionist community. Surprisingly, with the exception of recursive self organizing maps, unsupervised paradigms have been far less investigated. In particular, no algorithms for density estimation over graphs are found in the literature. This paper introduces first a formal notion of probability density function (pdf) over graphical spaces. It then proposes a maximum-likelihood pdf estimation technique, relying on the joint optimization of a recursive encoding network and a constrained radial basis functions-like net. Preliminary experiments on synthetically generated samples of labeled graphs are analyzed and tested statistically. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-04274-4_5 | ICANN (1) |
Keywords | Field | DocType |
unsupervised learning,supervised relational,density estimation,considerable attention,recursive encoding network,recursive self,formal notion,probability density function,graphical domains,recursive neural net,maximum-likelihood pdf estimation technique,connectionist community,maximum-likelihood connectionist model,neural net,maximum likelihood,radial basis function,relational learning | Density estimation,Pattern recognition,Statistical relational learning,Computer science,Self-organizing map,Unsupervised learning,Artificial intelligence,Artificial neural network,Probability density function,Connectionism,Machine learning,Recursion | Conference |
Volume | ISSN | Citations |
5768 | 0302-9743 | 3 |
PageRank | References | Authors |
0.40 | 4 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Edmondo Trentin | 1 | 286 | 29.25 |
| Leonardo Rigutini | 2 | 117 | 9.52 |