Statistical Hypothesis Testing versus Machine Learning Binary Classification: Distinctions and Guidelines

Texture Classification by Texton: Statistical versus Binary

Using statistical textons for texture classification has shown great success recently. The maximal response 8 (Statistical_MR8), image patch (Statistical_Joint) and locally invariant fractal (Statistical_Fractal) are typical statistical texton algorithms and state-of-the-art texture classification methods. However, there are two limitations when using these methods. First, it needs a training s...

Bayesian Hypothesis Testing in Machine Learning

Most hypothesis testing in machine learning is done using the frequentist null-hypothesis significance test, which has severe drawbacks. We review recent Bayesian tests which overcome the drawbacks of the frequentist ones.

Machine Learning, Neural and Statistical Classification

Algorithms which construct classifiers from sample data -such as neural networks, radial basis functions, and decision trees -have attracted growing attention for their wide applicability. Researchers in the fields of Statistics, Artificial Intelligence, Machine Learning, Data Mining, and Pattern Recognition are continually introducing (or rediscovering) induction methods, and often publishing ...

Machine Learning, Neural and Statistical Classification

Appendix : Machine Learning Bias Versus Statistical Bias

is if and 0 if. This high variance may help to explain why there is selection pressure for weak (machine learning) bias when the (machine learning) bias correctness is low. The reason that statisticians are interested in (statistical) bias and variance is that squared error is equal to the sum of squared (statistical) bias and variance. Therefore minimal (statistical) bias and minimal variance ...