In the training phase, the values of the model parameters (hyper-parameters) are optimized by grid search and k-fold cross-validation (CV) [35].
These requirements lead us to minimize the penalized log-likelihood function (3) where ln L is the log-likelihood function in (6), Q is a penalty function against the roughness of the ϕ-functions, and τ = (w1, w2) is a set of the weights for tuning parameters (hyper-parameters).
The prior over the biological variance is dependent on the mean expression level across conditions and the prior parameters (hyper-parameters) are learned from all of the data by fitting a nonparametric regression model.
The hyper-parameter (or regularization parameter) λ is a critical value which measures the trade-off between a good fit and a regularized solution.
We may represent the constraint in the form of the PDF of the fault parameters (varvec{theta }) with a hyper-parameter (alpha).
Step 3: Hyper-parameter optimization: Optimizing model parameters (C and ( g(frac{1}{{2sigma^{2} }}) )) using GSM, GA, PS, and GA SQP algorithms; Step 4: Validating the model and predicting the sulfur content.
Note that our method has the following tunable parameters, i.e., hyper-parameter (lambda ) (see Equation (9)), top |T| terms to generate embeddings of biomedical articles when applying Term Summation (# Terms) and top k articles in (D^k_{PDF}(Q)) (# PRF Documents).
To determine the best model to classify the lithology type, this study used validation curve to determine the parameter search range and adopted the hyper-parameter optimization method to obtain the best parameter set for each model.
a Hyper-parameter optimization using GS.
