Sentence examples for maximum likelihood machine from inspiring English sources

Various missing value imputation techniques have been employed by researchers, such as case-wise deletion, mean value imputation, maximum likelihood, machine learning algorithms including decision tree and MLP.

Bayesian Networks (BN), Categorical Bayesian Networks (CBN), Directed Acyclic Graph (DAG), Cross-Validation (CV), Maximum Likelihood (ML), Support Vector Machines (SVM), Kullback-Leibler (KL), Differential Expression (DE), Increasing Differential Expression (IDE).

To demonstrate its performance, the proposed classification is compared at the optimal segmentation level with Maximum Likelihood Classification and Support Vector Machine classification.

The classification experiments were carried out with three different classifiers: Support Vector Machine, Gaussian Maximum Likelihood with Leave-One-Out-Covariance estimator, and Linear Discriminant Analysis.

The main objective of this study focuses on the comparison of three classification tools for Landsat images, which are maximum likelihood classification (MLC), support vector machine and artificial neural network (ANN), in order to select the best method among them.

Compared to Maximum Likelihood (ML) and Support Vector Machine (SVM) classification, accuracy improvement achieved through use of the SEI DR procedure was, respectively, 6% and 5% and the differences were statistically significant (P < 0.05).

MLC (maximum likelihood classification) and SVM (support vector machine) are implemented for image classification.

The performance of different conventional and machine learning classifiers namely Malahanobis Distance (MD), Maximum Likelihood (ML), Artificial Neural Networks (ANNs), Support Vector Machines (SVMs) and Random Forests (RFs) was compared using the same datasets in the same IDL Interactive Data Languageenvironmentenvironment

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

Both the decision trees and support vector machines outperformed the maximum likelihood classifier (overall accuracy of 90 93%, compared to 65%), but the decision trees were superior at handling missing data.

The first objective is to compare the performance of the most notable supervised classification algorithms, including the maximum likelihood classifier (MLC) and the support vector machine (SVM).