O'Donoghue, J., Roantree, M.: A framework for selecting deep learning hyper-parameters. In: Maneth, S. (ed.) Data Science - 30th British International Conference on Databases, BICOD 2015, Edinburgh, UK, July 6-8, 2015, Proceedings, Lecture Notes in Computer Science, vol. 9147, pp. ...
We ran our first pruning study for 25,056 trials to cover approximately 5% of our hyperparameters search space. We ran it on the same search space as the one-shot HPO (see “Search space” section). As mentioned above, we used a random search (RandomSampler) ...
We tuned other hyperparameters using a grid search for achieving an optimal performance in the tuning data set. In addition, we used dropout to avoid model overfitting, a method that shuts down a random percentage of artificial neurons during each training epoch to reduce interdependent learning ...
Setting of hyperparameters The stepsize, regularization parameters and latent factor dimensions, for the above techniques have been tuned using cross-validation on training set (after hiding 10 % of the data) in each of the three cross-validation settings (see “Empirical evaluation”). The parame...
A study area in China was chosen as the case for evaluating the forest fire susceptibility. SVM, a benchmark and efficient machine learning method, was selected as the analysis method. Genetic algorithm (GA) was employed to compute the SVM parameters. The objectives of this study were: 1) ...
Approximate density functional theory has become indispensable owing to its balanced cost–accuracy trade-off, including in large-scale screening. To date, however, no density functional approximation (DFA) with universal accuracy has been identified, le
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The value of α and β parameters in the fitness function in Equation (18) are set by 0.99 and 0.01, and the value of parameter k is set with 5 in the k-NN classifier, respectively. As an evaluation criterion, all algorithms use classification accuracy and the number of selected features...
To train the classification model, we fine-tuned a pre-trained BERT model [33]. For pre-training, we used a pre-trained Japanese Wikipedia model [34] published by Tohoku University. The hyperparameters (batch size, dropout rate, learning rate, and number of epochs) were optimized by 1000 ...
(iv) a two-stage machine learning approach with adjusted parameters to achieve the highest overall accuracy on small object detection in the online VEDAI dataset, using its updated flowchart with an optimally tuned proportion of folders on training to testing and updating its structure of FC-...