Multi-attribute Selection for Salt Dome Detection Based on SVM and MLP Machine Learning Techniques

Risk Evaluation of Overseas Mining Investment Based on a Support Vector Machine

“…The assignment of other indexes is similar, and the assignment results are shown in Gu's reference [5]. According to the evaluation objectives and classification rules, the overall risk of overseas exploration and development of mining enterprises is divided into 5 grades by giving a mark, i.e., I (0-20), II (21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40), III (41-60), IV (61-80), V (81-100), which, respectively, represent no investment risk, serious investment risk, higher investment risk, general investment risk, and low investment risk [5]. (3) Sample set of evaluation models based on SVM A total of 40 datasets from 20 countries (North America: USA, Canada; Europe: Russia, Finland, Sweden, Turkey; Asia: Kazakhstan, Indonesia, Philippines, India; Latin America: Argentina, Peru, Brazil, Chile, Honduras, Guatemala; Africa: Zambia, Congo, Tanzania; and Oceania: Australia), collected in 2015 and 2016, provided by Gu [5], were used as learning samples; 32 samples from the first 16 countries were selected as training samples to construct the optimal support vector machine model, and 8 samples from the other 4 countries were used as test samples.…”

“…Third, the traditional linear method also has strong linear settings, but the impact of various factors on risk is often nonlinear [3][4][5]28]. In recent years, the support vector machine has emerged as an artificial intelligence method based on statistical learning theory [29][30][31][32], which has better generalization performance, has a global optimal solution, and can effectively solve the computational complexity of the linear model via kernel mapping and linearization etc. Using the expansion theorem of special kernel functions, the nonlinear mapping formula does not need to be computed, to some extent, the problem of "dimensionality disaster" is avoided.…”

“…The support vector machine (SVM) method is a new general-purpose machine learning method that was introduced within the framework of statistical learning theory, and it can realize the optimal classification of linear and nonlinear separable data [29][30][31][32][33][34][35][36][37].…”