A Review of Statistical Learning Machines from ATR to DNA Microarrays: design, assessment, and advice for practitioners
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Statistical Learning is the process of estimating an unknown probabilistic input-output relationship of a system using a limited number of observations; and a statistical learning machine (SLM) is the machine that learned such a process. While their roots grow deeply in Probability Theory, SLMs are ubiquitous in the modern world. Automatic Target Recognition (ATR) in military applications, Computer Aided Diagnosis (CAD) in medical imaging, DNA microarrays in Genomics, Optical Character Recognition (OCR), Speech Recognition (SR), spam email filtering, stock market prediction, etc., are few examples and applications for SLM; diverse fields but one theory. The field of Statistical Learning can be decomposed to two basic subfields, Design and Assessment. Three main groups of specializations-namely statisticians, engineers, and computer scientists (ordered ascendingly by programming capabilities and descendingly by mathematical rigor)-exist on the venue of this field and each takes its elephant bite. Exaggerated rigorous analysis of statisticians sometimes deprives them from considering new ML techniques and methods that, yet, have no "complete" mathematical theory. On the other hand, immoderate add-hoc simulations of computer scientists sometimes derive them towards unjustified and immature results. A prudent approach is needed that has the enough flexibility to utilize simulations and trials and errors without sacrificing any rigor. If this prudent attitude is necessary for this field it is necessary, as well, in other fields of Engineering.
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