Simulated Annealing-based Ontology Matching

Symbolic image matching by simulated annealing

In this paper we suggest an optimization approach to visual matching. We assume that the information available in an image may be conveniently represented symbolically in a relational graph. We concentrate on the problem of matching two such graphs. First we derive a cost function associated with graph matching and more precisely associated with relational subgraph isomorphism and with maximum ...

Simulated Annealing Based Pattern Classification

A method is described for finding decision boundaries, approximated by piecewise linear segments, for classifying patterns in ~N,N >~ 2, using Simulated Annealing (SA). It involves generation and placement of a set of hyperplanes (represented by strings) in the feature space that yields minimum misclassification. Theoretical analysis shows that as the size of the training data set approaches in...

Improved Simulated Annealing, Boltzmann Machine, and Attributed Graph Matching

Simulated annealing wavelet estimation via fourth-order cumulant matching

The fourth-order cumulant matching method has been developed recently for estimating a mixed-phase wavelet from a convolutional process. Matching between the trace cumulant and the wavelet moment is done in a minimum mean-squared error sense under the assumption of a non-Gaussian, stationary, and statistically independent reflectivity series. This leads to a highly nonlinear optimization proble...

A Mushy State Simulated Annealing

It is a long time that the Simulated Annealing (SA) procedure has been introduced as a model-free optimization for solving NP-hard problems. Improvements from the standard SA in the recent decade mostly concentrate on combining its original algorithm with some heuristic methods. These modifications are rarely happened to the initial condition selection methods from which the annealing schedules...