Biologists are confronting a problem they thought they had mostly solved: what, exactly, counts as life. A wave of ...

A Palestinian businessman on the persistent humanitarian crisis in the territory, and what he hopes might change.

Abstract: Support vector machines (SVMs) are popular learning algorithms to deal with binary classification problems. They traditionally assume equal misclassification costs for each class; however, ...

Institute for Information Systems (WIN), Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany Introduction: The analysis of discrete sequential data, such as event logs and customer ...

This repository contains two basic prediction models: Credit Card Fraud Detection and Titanic Survival Prediction. Both models demonstrate the use of machine learning for binary classification tasks.

The classification models built on class imbalanced data sets tend to prioritize the accuracy of the majority class, and thus, the minority class generally has a higher misclassification rate.

Abstract: Many data mining and pattern recognition tasks have a multi-class classification problem at their core. To give satisfactory results in operational contexts, certain applications necessitate ...

Multiclass classification is of great interest for various applications, for example, it is a common task in computer vision, where one needs to categorize an image into three or more classes. Here we ...

Because machine learning with deep neural techniques has advanced quickly, our resident data scientist updates binary classification techniques and best practices based on experience over the past two ...

Want smarter insights in your inbox? Sign up for our weekly newsletters to get only what matters to enterprise AI, data, and security leaders. Subscribe Now The first job for many artificial ...