Paper Info
Title
Finding MAPs Using High Order Recurrent Networks
Abstract
Belief revision is the problem of finding the most plausible explanation for an observed set of evidences. This has many applications in various scientific domains like natural language understanding, medical diagnosis and computational biology. Bayesian Networks (BN) is an important probabilistic graphical formalism used widely for belief revision tasks. In BN, belief revision can be achieved by setting the values of all random variables such that their joint probability is maximized. This assignment is called the maximum a posteriori (MAP) assignment. Finding MAP is an NP-Hard problem. In this paper, we are proposing finding the MAP assignment in BN using High Order Recurrent Neural Networks through an intermediate representation of Cost-Based Abduction. This method will eliminate the need to explicitly construct the energy function in two steps, objective and constraints, which will decrease the number of free parameters to set.
Year
DOI
Venue
2009
10.1007/978-3-642-10677-4_11
ICONIP (1)
Keywords
Field
DocType
intermediate representation,belief revision,np hard problem,recurrent neural network,computational biology,bayesian network,medical diagnosis,random variable
Random variable,Joint probability distribution,Computer science,Recurrent neural network,Natural language understanding,Bayesian network,Artificial intelligence,Maximum a posteriori estimation,Probabilistic logic,Belief revision,Machine learning
Conference
Volume
ISSN
Citations 
5863
0302-9743
2
PageRank 
References 
Authors
0.38
12
2
Name
Order
Citations
PageRank
Emad A. M. Andrews1182.74
Anthony J. Bonner2733422.63