Analyzing Boltzmann Machine Parameters for Fast Convergence

Authors:
Francisco Javier Salcedo;Julio Ortega;Alberto Prieto
Affiliations:
-;-;-
Venue:
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Bio-inspired Applications of Connectionism-Part II
Year:
2001

Citing 6
Cited 0

Simulated annealing and Boltzmann machines: a stochastic approach to combinatorial optimization and neural computing

Simulated annealing and Boltzmann machines: a stochastic approach to combinatorial optimization and neural computing
On the Approximation of NP-Complete Problems by Using the Boltzmann Machine Method: The Cases of Some Covering and Packing Problems

IEEE Transactions on Computers - Special issue on artificial neural networks
Parallel Coarse Grain Computing of Boltzmann Machines

Neural Processing Letters
Efficient learning in Boltzmann machines using linear response theory

Neural Computation
Register allocation by priority-based coloring

SIGPLAN '84 Proceedings of the 1984 SIGPLAN symposium on Compiler construction
Computers and Intractability: A Guide to the Theory of NP-Completeness

Computers and Intractability: A Guide to the Theory of NP-Completeness

Quantified Score

Hi-index	0.00

Visualization

Abstract

The behavior of a Boltzmann Machine (BM) according to changes in the parameters that determine its convergence is experimentally analyzed to find a way to accelerate the convergence towards a solution for the given optimization problem. The graph colouring problem has been chosen as a benchmark for which convergence with quadratic time complexity has been obtained.