Читать книгу Agricultural Informatics - Группа авторов - Страница 14

Machine learning can be studied under two vast categories called supervised and unsupervised learning. Supervised learning pertains to fact that data and process is supervised by supervisor. The process of training data is controlled to find the conclusions for new data. Some of the most commonly used techniques for supervised learning are Artificial neural network, Bayesian network, decision tree, support vector machines, ID3, k-nearest neighbor, hidden Markov model, etc. For unsupervised learning enormous volume of data is given as input to program for which the program generates patterns and identify the relations among them. Unsupervised learning can be used to discover the hidden patterns. K-nearest neighbor algorithm, self-organizing map methods, and partial based clustering techniques, hierarchical clustering approaches, k-means clustering, etc., belong to class of unsupervised learning. Predictive power of computers can be increased by integrating machine with statistics. Data scientists and analysts use this integration to predict trends from raw data that is fed into the system. The amount of data obtained in agricultural field is increasing enormously so the machine learning techniques can be applied to agricultural production for predicting crop related queries. Decisions regarding crop production can be made by using several available machines learning techniques. All such techniques use mathematics and stats for algorithm generation.