Theory & Practice
This is the website for Bill Dimm's forthcoming book, tentatively titled Predictive Coding: Theory & Practice.
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About the Book
Predictive coding is a form of technology-assisted review (TAR) where
supervised machine learning (learning by example) algorithms are used to predict
which documents are relevant based on analysis of training documents
tagged as relevant or non-relevant by a human reviewer, reducing
the time and expense required for document review.
This book explains how such algorithms work and how
to apply predictive coding effectively.
About the Author
Dr. Dimm is the founder and CEO of Hot Neuron LLC. He developed the algorithms for predictive coding, conceptual clustering, and near-dupe detection used in the company's Clustify software. He has over two decades of experience in the development and application of sophisticated mathematical models to solve real-world problems in the fields of theoretical physics, mathematical finance, information retrieval, and e-discovery. Prior to starting Hot Neuron, he did derivative securities research at Banque Nationale de Paris. He has a Ph.D. in theoretical elementary particle physics from Cornell University.