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Enlighted by subdomain adaptation methods, this paper designs a novel subdomain adaptative deep network (SADN) for excavating diagnosis knowledge from source domain datasets. Firstly, the convolutional layer, residual blocks and SE-Residual blocks are utilized for extracting meaningful deep features ...
In the SGD, the gradient of the hinge loss is computed at each sample and the model is iteratively updated using a decreasing learning rate. As it is standard in SVM, we use the squared euclidean norm L2 of the model parameter vector as a regularizer to shrink the parameters towards the ...
in this paper, an improvement model is proposed using WordNet lexical ontology and BERT to perform deeper learning on the features of text, thereby improving the classification effect of the model. It was observed that classification success increased when using WordNet 11 general lexicographer files...
Then, the sample vial containing the obtained paste was lowered into the micro reaction calorimeter for analysis. 2.3. Parallel Plate Rheometry The development of yield stress in the fresh cementitious pastes was measured as a function of time with a hybrid rheometer (DHR-2, TA Instruments, New...
The precision measures the model’s accuracy in classifying a sample as positive, as shown in Equation (2): Precision = TPTP+FP,Precision = TPTP+FP, (2) The recall is calculated as the ratio between the numbers of positive samples correctly classified as positive to the total number of ...
T is the total number of leaf nodes, and q represents the arrangement of each tree that links a sample to its matching leaf node. As a result, the anticipated value of XgBoost is equal to the total of each tree's leaf node values. This model aims to minimize the goal function below ...