How do Bayesian methods in machine learning help with the problem of limited data? Can this be used for image classification? When reading about machine learning, I've often come across information stating that Bayesian methods in machine learning are effective when you only possess a limited amount of data. As someone who is planning to start learning machine learning, I was curious how it accomplishes this? Lastly, and more specifically, I was especially wondering how and/or if this can be applied to problems such as image classification, when there is a limited amount of data (limited number of images to train the algorithm on)?
Any textbook, research, or other references with regards to the latter would also be greatly appreciated.
Thanks.
The discussion of Bayesian reasoning and its advantages over frequentist reasoning is very wide. I refer you to the book “Pattern recognition and machine learning” by Cristopher Bishop which is a great book on Bayesian reasoning.