High-speed hammer impacts materials to crush materials. There are two ways of crushing (Wet and dry)
Jun 20, 2014 In Zimbabwe, Miombo woodlands cover approximately 42% of the country applied machine learning classifiers for mapping land cover in the
Jun 20, 2014 Zimbabwe; Miombo woodlands; Landsat 8; decision trees; random forests;  successfully applied machine learning classifiers for mapping
Jun 20, 2014 Therefore, the RF classifier has potential to improve woodland cover mapping in the Cover in the Miombo Ecosystem: A Comparison of Machine Learning Classifiers cover mapping in the Miombo ecosystem in Zimbabwe.
Taking Harare metropolitan province in Zimbabwe as an example, we , X. Yang, “Parameterizing Support Vector Machines for Land Cover Classification,”
Feb 28, 2017 In machine learning and statistics, classification is a supervised learning approach in which the computer program learns from the data input
Jul 17, 2015 Within artificial intelligence AI, machine learning has emerged as the Whatever the learning algorithm, a key scientific and practical goal is
Conservation implications: Vegetation classification and mapping are useful tools for of Zimbabwe alongside the border with Mozambique, stretching between the .. Machine Learning 45, 5–32. http:dx.doi.org10.1023A:1010933404324.
May 31, 2017 The software behind this is given some classifiers which will help it I dont know how many companies in Zimbabwe are using machine
Classification is used to assign items to a discrete group or class based on a specific set of features. Classification algorithms are a core component of statistical
Jun 28, 2018 Hello Reader, This is my second blog post in the journey of discussing the important concepts in Machine learning. This blog post will give you
Dec 11, 2017 Questions like this are a symptom of not truly understanding the difference between classification and regression and what accuracy is trying to
Sep 23, 2015 Machine learning is a great approach for many text classification problems. For example, the problem of classifying an email as “spam” or “not
Analyze text to extract meta-data from content such as concepts, entities, keywords and more.
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