Paper Info
Title
Recognition of sequences of graphical patterns
Abstract
Several real-world problems (e.g., in bioinformatics/proteomics, or in recognition of video sequences) can be described as classification tasks over sequences of structured data, i.e. sequences of graphs, in a natural way. This paper presents a novel machine that can learn and carry out decision-making over sequences of graphical data. The machine involves a hidden Markov model whose state-emission probabilities are defined over graphs. This is realized by combining recursive encoding networks and constrained radial basis function networks. A global optimization algorithm which regards to the machine as a unity (instead of a bare superposition of separate modules) is introduced, via gradient-ascent over the maximum-likelihood criterion within a Baum-Welch-like forward-backward procedure. To the best of our knowledge, this is the first machine learning approach capable of processing sequences of graphs without the need of a pre-processing step. Preliminary results are reported.
Year
DOI
Venue
2010
10.1007/978-3-642-12159-3_5
ANNPR
Keywords
Field
DocType
classification task,novel machine,hidden markov model,bare superposition,global optimization algorithm,graphical data,graphical pattern,baum-welch-like forward-backward procedure,structured data,pre-processing step,maximum-likelihood criterion,machine learning,baum welch,global optimization,maximum likelihood,relational learning,radial basis function network
Conditional random field,Superposition principle,Radial basis function,Pattern recognition,Statistical relational learning,Computer science,Artificial intelligence,Hidden Markov model,Data model,Machine learning,Recursion,Encoding (memory)
Conference
Volume
ISSN
ISBN
5998
0302-9743
3-642-12158-6
Citations 
PageRank 
References 
0
0.34
7
Authors
3
Name
Order
Citations
PageRank
Edmondo Trentin128629.25
Shujia Zhang2433.05
Markus Hagenbuchner370643.30