The goal of a machine learning binary classification problem is to predict a variable that has exactly two possible values. For example, you might want to predict the sex of a company employee (male = ...

ABSTRACT: Accurate prediction of malaria incidence is indispensable in helping policy makers and decision makers intervene before the onset of an outbreak and potentially save lives. Various ...

Abstract: We consider a human-automation team jointly solving binary classification tasks over multiple time stages. At each stage, the automation observes the data for a batch of classification tasks ...

This jupyter notebook tutorial is meant to be a general introduction to machine and deep learning. We use seismic time series data from i) real earthquakes and ii) nuisance signals to train a suite of ...

Quantum computers have the unique ability to operate relatively quickly in high-dimensional spaces—this is sought to give them a competitive advantage over classical computers. In this work, we ...

Binary classification of Diabetic Retinopathy using SVM in MATLAB with fundus image features. This repository compares the performance of Adaline, Logistic Regression, and Perceptron models on binary ...

Lexical simplification systems replace complex words with simple ones based on a model of which words are complex in context. We explore how users can help train complex word identification models ...

In this tutorial, you will download a version of TensorFlow that will enable you to write the code for your deep learning project in Python. On the TensorFlow installation webpage, you’ll see some of ...

This tutorial outlines a complete workflow for classifying cropland land cover using Landsat 8 imagery and version 2.3.2 of the Semi-Automatic Classification Plugin (SCP) for QGIS. The study area is ...