A support vector machine (SVM) is a type of supervised learning algorithm used in machine learning to solve classification and regression tasks. SVMs are particularly good at solving binary classification problems, which require classifying the elements of a data set into two groups....
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To address these limitations, we propose the SVMBN algorithm for predicting metabolite-disease associations. The proposed approach involves the following steps: First, six similarity calculation methods are employed to construct the metabolite similarity network and the disease similarity network separately....
Optimization routine, specified as the comma-separated pair consisting of 'Solver' and a value in this table. ValueDescription 'ISDA' Iterative Single Data Algorithm (see [3]) 'L1QP' Uses quadprog (Optimization Toolbox) to implement L1 soft-margin minimization by quadratic programming. This optio...
For two-class learning, the software implements robust learning. In other words, the software attempts to remove 100*outlierfraction% of the observations when the optimization algorithm converges. The removed observations correspond to gradients that are large in magnitude. For one-class learning, the...
Fig. 1 shows the various steps involved in SVM techniques which are image acquisition, image pre-processing with discrete cosine transform (DCT) domain and color space conversion, image segmentation with the K-means clustering algorithm, feature extraction by LBP feature, and GLCM. The images were...
Prior experience in programming is required to fully understand the implementation of machine learning algorithm taught in the course. However, Python programming knowledge is optional. If you want to be able to code and implement the machine learning strategies in Python, then you should be able ...
The process of extracting rules from the training dataset using the LIME explainer is delineated in Algorithm 1. Initially, the training data (X_ train) were annotated with the target (Y_train) by employing the SVM model, and a descriptor of this dataset encompassing all its features was ...
This study addresses the critical need for effective groundwater (GW) management in Muzaffarabad, Pakistan, amidst challenges posed by rapid urbanization and population growth. By integrating Support Vector Machine (SVM) and Weight of Evidence (WOE) techniques, this study aimed to delineate GW pote...