With the increased focus within the discipline of eDiscovery on Technology-Assisted Review, three references are provided to help legal professionals establish a solid base of definitional and contextual information for considering machine learning.
Reference #1: Book: New Advances in Machine Learning. Chapter: Types of Machine Learning Algorithms. Author: Taiwo Oldipupo Ayodele (University of Portsmouth, United Kingdom).
Reference #2: Video: Lectures on Machine Learning. Lecturer: Andrew Nq (Director , Stanford Artificial Intelligence Lab, Stanford University).
Available via Video Series Link: https://class.coursera.org/ml/lecture/preview
Reference #3: Video Lectures on Machine Learning. Lecturer: Pedros Domingos (Professor of Computer Science & Engineering, University of Washington.
Available via Video Series Link: https://class.coursera.org/machlearning-001/lecture/preview
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Since its 2007 introduction, kCura’s Relativity product has become one of the world’s leading attorney review platforms. One of the elements of Relativity’s strong growth and marketplace acceptance has been kCura’s focus on and support of partnerships. Provided as a by-product of review platform research and presented in the form of a simple and sortable table is an aggregation of kCura Premium Hosting Partners and Consulting Partners.
Taken from a combination of public market sizing estimations as shared in leading electronic discovery reports, publications and posts over time, the following eDiscovery Market Size Mashup shares general worldwide market sizing considerations for both the software and service areas of the electronic discovery market for the years between 2013 and 2018.
Provided as a non-comprehensive overview of key and publicly announced eDiscovery related mergers, acquisitions and investments to date in 2014, the following listing highlights key industry activities through the lens of announcement date, acquired company, acquiring or investing company and acquisition amount (if known).
Grossman and Cormack concluded that CAL demonstrated superior performance over SPL and SAL, while avoiding certain other problems associated with these traditional TAR 1.0 protocols. Specifically, in each of the eight case studies, CAL reached higher levels of recall (finding relevant documents) more quickly and with less effort that the TAR 1.0 protocols.
Grossman and Cormack argue, attorneys should rely on scientific studies of the efficacy of CAR/TAR systems based on an analogy to the Daubert standard. They argue that evaluating the success of eDiscovery is burdensome and can be misleading. They liken the process of eDiscovery to that of roasting a turkey.
The results presented here do not support the commonly advanced position that seed sets, or entire training sets, must be randomly selected [19, 28] [contra 11]. Our primary implementation of SPL, in which all training documents were randomly selected, yielded dramatically inferior results to our primary implementations of CAL and SAL, in which none of the training documents were randomly selected.