Learn how data scientists use Bayesian methods to tackle image-related problems, such as segmentation, classification, reconstruction, and super-resolution.

Naive Bayes classification is a machine learning technique that can be used to predict the class of an item based on two or more categorical predictor variables. For example, you might want to predict ...

Bayesian Sampling of Virtual Examples to Improve Classification Accuracy Abstract: A virtual example is an artificial example that does not exist in the given training set. We sample a virtual example ...

Classification is a supervised learning task where the goal is to assign a category label to new observations based on a training dataset of labeled examples. Bayesian classification uses probability ...

Building a Bayesian deep learning classifier Intro In this blog post, I am going to teach you how to train a Bayesian deep learning classifier using Keras and tensorflow. Before diving into the ...

For example, in image classification tasks, the differences in processing strategies by humans and machine classifiers lead to different types of errors made by each, even though their overall level ...

A virtual example is an artificial example that does not exist in the given training set. We sample a virtual example from a Bayesian network constructed with the original training set. The usefulness ...

Naive Bayes classification is a machine learning technique that can be used to predict the class of an item based on two or more categorical predictor variables. For example, you might want to predict ...