![]() Structural risk minimization with statistics and learning methods is the foundation of the SVM algorithms. With these mathematical properties, SVM algorithms can handle even large datasets well. The advantages include for example sparseness of solution, flexibility for large feature spaces, and outlier handling capabilities. The mathematical properties of the SVM algorithms are found to be robust (Brown et al. Support vector machines for classification (SVC) and support vector regression (SVR) are the two main types of SVM algorithms. The development of the SVMs algorithms has clearly focused on industrial applications (Smola and Scholkopf. Then, adaptive control theory is utilized for the convergence in finite time for application in sliding mode dynamics with disturbances and non-linearity issues.Īt AT&T Bell Laboratories, Vapnik and colleagues, Boser and Guyon, initiated the studies of the support vector machines (SVMs) algorithms. Recently, the authors in proposed an integration of RBF neural networks and a passivity control framework based on the sliding mode theory for offshore dock cranes, modeled as non-linear systems. ![]() If these conditions are not satisfied, these three procedures will be iterated until the conditions are met. Thirdly, the new results from the modified model are evaluated with the pre-defined conditions. Secondly, the results from the comparison engine are utilized for assigning modifications to the underlying model. Firstly, a comparison engine is employed for checking the input data with the underlying model. Despite the diverse background difference, these approaches usually have the following common procedures. Inductive logic, evolutionary computing, artificial neural networks, the Bayesian approach, and Markov chains are only a few examples. Machine learning includes methods based on statistical analysis, mathematical modeling, control theory and computational intelligence. Machine learning methods can extract meaningful and valuable outputs from the patterns in the data. Deep learning refers to machine learning algorithms with multi-layer structures for processing higher-level characters from the input dataset. Other machine learning approaches include semi-supervised learning, reinforcement learning, self-learning, robot learning and association rule learning, which are not covered in this review for sensor applications. These methods can be employed to extract valuable information from the datasets. Clustering algorithms are used to group data without any pre-defined classes. Machine learning includes, for example, supervised learning such as classification and regression, and unsupervised learning such as clustering, and dimensionality reduction. Data mining algorithms are developed to automate the feedback process to overcome the disadvantages of manual feedbacks, with the goal of discovery of unknown features in the data, while machine learning usually needs known features learned in the training process for prediction. While the feedback can be made by humans, this can be time consuming and labor intensive. Machine learning algorithms can be very useful for knowledge discovery, with the building of models based on training data The knowledge discovery process of machine learning algorithms usually involves feedback at each iteration with the goal that further improvement can be achieved.
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