This paper proposes a novel end-to-end deep neural network architecture and adopts Gumbel distribution as an activation function in neural networks for class imbalance problem in the application of ...

Abstract: In recent years, deep neural networks ... for any binary classification problem quickly, we hope that our research contributes to discovering intelligent algorithms for optimizing ...

Wrapping Up Binary classification is arguably the most fundamental problem in machine learning. There are several alternatives to using a neural network. Logistic regression is perhaps the most common ...

But regardless of this enormous variety, all neural networks are applied to solving user problems and making the predictions needed by a wide range of uses cases. Deep learning systems use ...

Deep learning ... based neural networks. Random Forests, also known as Random Decision Forests, which are not neural networks, are useful for a range of classification and regression problems.

However, early melanoma classification works can only use the low-level information of images, so the melanoma cannot be classified efficiently; the recent deep ... through neural networks. In detail, ...

Learn More The use of deep learning ... depends on the problem at hand and that’s what determines the neural network architecture. If we are interested in image classification, then we use ...

Machine learning with neural networks is sometimes said to be part art and part science. Dr. James McCaffrey of Microsoft Research teaches both with a full-code, step-by-step tutorial. A binary ...