Machine Learning in Healthcare - Application of Advanced Computational Techniques to Improve Healthcare
This review article, which serves as the introduction to the special session on deep learning, provides state-of-the-art models and summarizes current understanding on this type of learning method, which is used to tackle a variety of difficult categorization tasks.
Abstract
Since 2006, Deep Learning, also known as Hierarchical Learning, has developed as a new field of research in Machine Learning. Deep learning models are used to solve problems that shallow architectures (e.g., regression) cannot solve due to the curse of dimensionality. Automatically created statistically robust characteristics are derived from the data using a two-stage learning procedure that incorporates multiple layers of nonlinear processing. This review article, which serves as the introduction to the special session on deep learning, provides state-of-the-art models and summarizes current understanding on this type of learning method, which is used to tackle a variety of difficult categorization tasks. Deep Learning is a relatively recent area of research in Machine Learning that was founded with the purpose of getting Machine Learning closer to one of its original objectives: Artificial Intelligence. Deep Learning is concerned with the acquisition of several levels of representation and abstraction that aid in the interpretation of various forms of data, including images, audio, and text.