To estimate the type of fertilizer based on soil minerals using voting classifier. For forecasting accuracy %, a Voting Classifier with sample size 10 and Decision Tree was iterated at various times. A supervised learning algorithm is Tree. It constructs “forest” an array decision trees, typically trained to use “bagging” method. Novel Classification predictive model that learns from several mo...

In this paper, we consider a semi-supervised approach to the problem of track classification in dense 3D range data. This problem involves the classification of objects that have been segmented and tracked without the use of a class-specific tracker. We propose a method based on the EM algorithm: iteratively 1) train a classifier, and 2) extract useful training examples from unlabeled data by e...

This paper presents a new approach to the unsupervised training of Bayesian network classifiers. Three models have been analysed: the Chow and Liu (CL) multinets; the treeaugmented naive Bayes; and a new model called the simple Bayesian network classifier, which is more robust in its structure learning. To perform the unsupervised training of these models, the classification maximum likelihood ...

There are some prevalent problems in the classification of hyperspectral remote sensing imagery currently, such as many bands, large amount of data, high proportion of mixed pixels and lower spatial resolution and so no. In order to solve the above problems, the sequential minimal optimization (SMO) algorithm is researched, and a supervised classification method based on binary decision tree SM...

Conventional image classification methods restricts each pixel of data set to exclusively just one cluster. As a consequence, with this approach the classification results are often very crispy, i.e., each pixel of the image belongs to exactly just one class. However, in many real situations, for images, issues such as limited spatial resolution, poor contrast, overlapping intensities, and nois...