… enables the online lending platform to build an intelligent risk control model by using multi-…
NN to construct a forecasting model and compares the results. The XGBoost algorithm has a …

… SVM is the best supervised learning method at present and can deal with the … comparative
study for content-based dynamic spam classification using four machine learning algorithms…

… This paper introduces a variety of artificial intelligence … classification based on deep learning,
and the analysis data can be … the multi task analyses with deep learning algorithms on multi-…

… In this section, we first examine the type of information that creates the comparative advantage
among financial analysts in making earnings forecasts. We then provide validation tests to …

… For this purpose, from the investigation of relevant literatures and latest research results, the
characteristics of unsupervised learning algorithms are highlighted. At present, the topology …

… Supervised learning is a machine learning technique in which a … regression analysis and
machine learning algorithms proposed with artificial neural networks. This comparative review …

… In the current study, a machine learning-based slope safety and stability … The comparative
analysis of algorithms ’ prediction effects established XGBoost as the best evaluation model, …

… supervision received during training, machine learning can be classified into supervised
learning, unsupervised learning… Comparison of machine learning techniques to predict all-…

… The comparison of different algorithms helps identify the most … In conclusion, machine
learning algorithms hold great promise in … Supervised learning algorithms are widely used in …

… this review, we provide an overview of existing neuroimaging technologies and supervised
and unsupervised machine learning … since SVM is robust in comparison to linear regression, …