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In both the reconstruction and recognition methods, the analysis consisted of two steps: (1) training the algorithm and (2) decoding using the trained algorithm.
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It was found that of the three models, ANN model gives the most accurate results using the training algorithm Levenberg Marquardt (LM).
One can directly use the training algorithm to find the best feature extractors by adjusting the weights.
The first ring contains eight nodes which are trained using the SOM training algorithm: In each step, a training example x is selected and the prototype vectors { u k ) k =1,…,7} are searched for the best matching unit (BMU) u, with and the learning rule is applied to the nodes.
The neural network was trained using the Levenberg Marquardt training algorithm in combination with Bayesian regularization.
Total 205 atomic parameters were optimized with the standard genetic algorithm using the training set consisting of 139 organic molecules with varying shapes and functional groups.
We performed 500 runs of the Gibbs sampling algorithm using the training set, and retained the 50 runs having reached the highest likelihood values.
Each of the thirty-two spots in the data set was automatically segmented twice, once using the algorithm trained on pathologist A's manual segmentation masks and once using the algorithm trained on pathologist B's manual segmentation masks.
The AMs were trained on uncompressed speech using the Viterbi training algorithm from 72 h of speech and 555 speakers.
Overall sensitivity for colorectal cancers was comparable in both studies as identified using the original training algorithm.
A two-steps training algorithm is used: the RCE training algorithm for the prototype's parameters, and the multivariate linear regression for the output connection weights.
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