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Training algorithm for transductive Support Vector Machines.
This table shows error rates obtained by our training algorithm.
Besides, a specially scheduled two phase training algorithm is adopted.
The performance of the parallel DEKF training algorithm is studied.
The optimal ANN architecture and training algorithm were determined.
A training algorithm based on a combination of backpropagation and gaussian random guesses was applied.
What determines the efficiency of the training algorithm, and how many training data it will require?
A Training Algorithm for Statistical Sequence Recognition with Applications to Transition-Based Speech Recognition.
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.
Then a highly efficient training algorithm is proposed based on two strategies.
The training algorithm is guaranteed to converge and is very efficient.
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