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In order to work out the proper methodology of obtaining spectral reflectance coefficients from hyperspectral and multispectral images, it is necessary to verify
Here, the quality metrics evaluation on hyperspectral images has been presented using k-means clustering and segmentation.. After classification the assessment of similarity
Untuk akurasi yang lebih baik, penelitian ini memprediksi stroke dengan menggunakan dua algoritma, yaitu support vector machine (SVM) dan logistic regression (LR).. Pada
Each value of the principal component analysis PCA is trained using the usual support vector machine SVM algorithm and the firefly algorithm-support vector machine FA-SVM algorithm..
Then Some classic Machine Learning algorithms like Naive Bayes, Random Forest, Logistic Regression, K-Nearest Neighbors, Support Vector Machine SVM, Decision Tree and Neural Network
KNN, Logistic regression, Support Vector Machine SVM, Gaussian Naïve Bayes, Decision tree, Random forest, Multilayer perception MLP-ANN, ADA boosting classifier, Latent Dirichlet
Then, four different models, SVMsupport vector machine,GS-SVM, GA-SVM and PSO-SVM, were used to establish the hyperspectral apple classification prediction model, and different kernel
Confusion Matrix Metode Support Vector Machine SVM pada Data latih Hasil kinerja accuracy, precision dan recall dari metode Multi Layer Perceptron MLP dan Support Vector Machine SVM