Sentence examples for machine to classify from inspiring English sources

In the training stage, we split the image classification into many one-vs.-one classification problems; a set of binary classifiers as weak learners are learned on a local machine, to classify each image in an autonomous system.

The feature selection is combined then with a support vector machine to classify data.

The proposed feature selection is then combined with a support vector machine to classify the input data.

Methods We used three non linear supervised machine learning approaches (multilayer perceptron neural network, decision tree and gaussian support vector machine) to classify responders vs non responders from a dataset of 115 patients using Matlab R2015b software.

This paper presents a method based on Histogram of Oriented Gradients to extract features of an image box containing the tracked object and Support Vector Machine to classify moving objects in crowded traffic scenes.

We have used support vector machine to classify the images with the one-vs-all paradigm and have selected the least squares training algorithm and a linear kernel to find the decision boundary due to the characteristics of the data set.

We discuss the use of Support Vector Machines to classify sound recordings according to the taxonomy and describe two use cases for the obtained classification models: a content-based web search interface for a large audio database and a method for segmenting field recordings to assist sound design.

Mining the internet, social networks and Wikipedia, researchers have created large collections of images and text, enabling machines to classify images, recognize speech, and translate language.

Yu et al. [19] created a bioscience image taxonomy (consisting of Gel-Image, Graph, Image-of-Thing, Mix, Model, and Table) and used Support Vector Machines to classify the figures, using properties of both the textual captions and the images.

Therefore, we set a threshold of 350 votes (of 400 machines) to classify a gene as essential for S. typhimurium and obtained a comparable amount of 128 predicted essential genes.

This article proposes an effort to apply the multi-class support vector machine classifiers to classify the supraspinatus image into different disease groups that are normal, tendon inflammation, calcific tendonitis and supraspinatus tear.