Sentence examples for maximum likelihood classifier from inspiring English sources

A pixel-based supervised classification with maximum likelihood classifier was used to classify images.

Using a maximum likelihood classifier, vegetation communities were classified into five classes (successional stages) of grass, grass shrub, shrub, tree shrub, and forest.

Next, a standard, supervised image classification approach (i.e. Maximum Likelihood Classifier) was employed to classify two major land covers of interest: agricultural land and settlement (urban) areas.

Then we classify the images using Maximum Likelihood Classifier (MLC) and Support Vector Machines (SVM) classifier.

All combined bands were classified using a supervised maximum likelihood classifier.

The supervised classification technique by maximum likelihood classifier has been employed to create a classified image and has been assessed based on Kappa index.

Comparative simulation studies revealed the superiority of proposed method compared to WF based fuzzy explicit, fuzzy k-nearest neighbor and fuzzy maximum likelihood classifiers under noisy environment.

In this context, two techniques are presented for the unsupervised updating of the parameters of a maximum-likelihood classifier and a radial basis function neural-network classifier, on the basis of the distribution of the new image to be classified.

We built a maximum-likelihood classifier using the 101 tumour samples and evaluated the classifier using a 'leave-one-out' cross-validation scheme.

We compared the maximum-likelihood classifier with CARROT using a set of 100 simulated pairs of phased individuals for each relationship of degree up to five, including all rotated relationships.

Learning of the embedded WM data is achieved in a principal component analysis (PCA) transformed wavelet space and a maximum likelihood (ML) classifier is designed for WM decoding.