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Evaluating E-Discovery

William Webber's E-Discovery Consulting Blog. Sampling with zero intent. March 15th, 2018. A zero intent sample is a sample which will only satisfy our validation goal if no positive examples are found in it. If we have a population (in e-discovery, typically a document set) where one in R instances are positive (one in R documents relevant), and we only want a one in Q probability of sampling a positive instance, then our sample size can be no more than R / Q. Read the rest of this entry ». Of estimatin...

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William Webber's E-Discovery Consulting Blog. Sampling with zero intent. March 15th, 2018. A zero intent sample is a sample which will only satisfy our validation goal if no positive examples are found in it. If we have a population (in e-discovery, typically a document set) where one in R instances are positive (one in R documents relevant), and we only want a one in Q probability of sampling a positive instance, then our sample size can be no more than R / Q. Read the rest of this entry ». Of estimatin...

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Evaluating E-Discovery | blog.codalism.com Reviews

https://blog.codalism.com

William Webber's E-Discovery Consulting Blog. Sampling with zero intent. March 15th, 2018. A zero intent sample is a sample which will only satisfy our validation goal if no positive examples are found in it. If we have a population (in e-discovery, typically a document set) where one in R instances are positive (one in R documents relevant), and we only want a one in Q probability of sampling a positive instance, then our sample size can be no more than R / Q. Read the rest of this entry ». Of estimatin...

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Off to FTI: see you on the other side « Evaluating E-Discovery

http://blog.codalism.com/index.php/off-to-fti-see-you-on-the-other-side

William Webber's E-Discovery Consulting Blog. Laquo; Confidence intervals on recall and eRecall. Off to FTI: see you on the other side. This entry was posted on Sunday, January 18th, 2015 at 7:49 pm and is filed under Uncategorized. You can follow any responses to this entry through the RSS 2.0. Feed You can leave a response. From your own site. 2 Responses to “Off to FTI: see you on the other side”. January 20, 2015 at 8:33 am. March 5, 2015 at 2:21 am. Click here to cancel reply.

2

Total review cost of training selection methods « Evaluating E-Discovery

http://blog.codalism.com/index.php/total-review-cost-of-training-selection-methods

William Webber's E-Discovery Consulting Blog. Laquo; Finite population protocols and selection training methods. Total assessment cost with different cost models. Total review cost of training selection methods. In this post, I am going to evaluate three selection methods— random, uncertainty, and relevance. Let's begin with the cost curves for a particular topic, Topic E41. Relative cost for 80% recall curve for Topic E41. Near-)minimum cost on this set of runs. Considering the cost curves themselves, t...

3

Total assessment cost with different cost models « Evaluating E-Discovery

http://blog.codalism.com/index.php/total-assessment-cost-with-different-cost-models

William Webber's E-Discovery Consulting Blog. Laquo; Total review cost of training selection methods. Why training and review (partly) break control sets. Total assessment cost with different cost models. In my previous post. With this differentiated cost model, we get the following average assessment costs (see my previous post for the interpretation of this table):. 005% - 0.5%. As one might expect, relevance selection (which aims to do much or all of the review effort during training) becomes more exp...

4

Confidence intervals on recall and eRecall « Evaluating E-Discovery

http://blog.codalism.com/index.php/confidence-intervals-on-recall-and-erecall

William Webber's E-Discovery Consulting Blog. Laquo; Why training and review (partly) break control sets. Off to FTI: see you on the other side. Confidence intervals on recall and eRecall. However, to avoid contaminating the control with the training set. Using the control set for the final estimate is also open to the objection that the control set coding decisions, having been made before the subject-matter expert (SME) was familiar with the collection and the case, may be unreliable. Direct method: a ...

5

Assessor error and term model weights « Evaluating E-Discovery

http://blog.codalism.com/index.php/assessor-error-and-term-model-weights

William Webber's E-Discovery Consulting Blog. Laquo; Annotator error and predictive reliability. Can you train a useful model with incorrect labels? Assessor error and term model weights. In my last post. Is the relevance (for training), or relevance score (for predicting);. Is the number of times the. Th word in the collection's vocabulary occur in the document in question; and. Is the weight, learnt by the classifier from the training data, that attaches to this. Let's start with the term weights from ...

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papers « NonRelevant

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Database and Information Retrieval. Archive & Links. Assessing Airbnb’s Prospects in its San Francisco Litigation. Refunds for Minors, Parents, and Guardians for Purchases of Facebook Credits. Preventing Discrimination at Airbnb. How Uber Uses API Restrictions to Block Price Comparison and Impede Competition. SIGIR 2011 ACCEPTED PAPERS THREAD. On June 1st, 2011. Please visit Ian’s post on Not Relevant for pre-prints of SIGIR 2011 accepted papers. Rarr; Read More: SIGIR 2011 ACCEPTED PAPERS THREAD. The ag...

blog.cluster-text.com

Resources | Clustify Blog – eDiscovery, Document Clustering, Predictive Coding, Information Retrieval, and Software Development

https://blog.cluster-text.com/resources

Clustify Blog – eDiscovery, Document Clustering, Predictive Coding, Information Retrieval, and Software Development. Thoughts on e-discovery, computers, and software development. Complex Discovery: eDiscovery Resources. 8211; categorized list of ediscovery resources. Complex Discovery: TAR Articles. 8211; chronological list of of articles on technology assisted review from many sources. 8211; Gerard Britton’s blog on analytics and ediscovery. DESI Workshop Papers IV. 8211; William Webber’s blog. Highligh...

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William Webber's Home Page

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Phone: 61 (0)418 532 084. My research page (about to move). I'm an IT consultant and researcher. Based in Melbourne, Australia. You can contact me at william@williamwebber.com. Or phone me on 61-0418-532-084. A much more interesting page than this is maintained by my good friend Danny Calegari. Last Modified: Tue Dec 3 02:46:13 UTC 2013.

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Danny Calegari | Geometry and the imagination

https://lamington.wordpress.com/author/dannycaltech

Geometry and the imagination. Author Archives: Danny Calegari. Stiefel-Whitney cycles as intersections. February 4, 2016. This quarter I’m teaching the “Differential Topology” first-year graduate class, and for a bit of fun, I decided to teach an introduction to characteristic classes, following the classic book of that name by Milnor and Stasheff. The book begins with … Continue reading →. Schläfli – for lush, voluminous polyhedra. April 28, 2015. Volume change under Dehn filling. March 15, 2015. The re...

danasresearchblog.blogspot.com

Dana Zhang's Research Blog: Back Again

http://danasresearchblog.blogspot.com/2009/02/another-internship.html

Dana Zhang's Research Blog. A PhD candidate's random collection of research notes that may or may not be related to data mining and role based access control. Sunday, February 01, 2009. Subscribe to: Post Comments (Atom). I am a PhD student with research interests in data mining and role based access control in Melbourne, Australia. My primary supervisor: Rao Kotagiri. My secondary supervisor: Rui Zhang. My industry sponsor: CA. My university: The University of Melbourne. A nice Web mining book.

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Dana Zhang's Research Blog: RoleMiner: Mining Roles using Subset Enumeration

http://danasresearchblog.blogspot.com/2006/09/caroleminer-mining-roles-using-subset.html

Dana Zhang's Research Blog. A PhD candidate's random collection of research notes that may or may not be related to data mining and role based access control. Tuesday, September 19, 2006. RoleMiner: Mining Roles using Subset Enumeration. Author = {Jaideep Vaidya, Vijayalakshmi Atluri, and Janice Warner},. Title = {RoleMiner: Mining Roles using Subset Enumeration},. Booktitle = {CCS '06: Proceedings of the 13th ACM Conference on Computer and Communications Security},. Year = {2006},. Uses each permission ...

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Dana Zhang's Research Blog: Role Engineering using Graph Optimisation

http://danasresearchblog.blogspot.com/2007/08/role-engineering-using-graph.html

Dana Zhang's Research Blog. A PhD candidate's random collection of research notes that may or may not be related to data mining and role based access control. Thursday, August 09, 2007. Role Engineering using Graph Optimisation. Author = {Dana Zhang and Ramamohanarao Kotagiri and Tim Ebringer},. Title = {Role Engineering using Graph Optimisation},. Booktitle = {SACMAT '07: Proceedings of the twelth ACM symposium on Access control models and technologies},. Year = {2007},. Publisher = {ACM Press},. The pr...

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Dana Zhang's Research Blog: Migrating to Optimal RBAC with Minimal Perturbation

http://danasresearchblog.blogspot.com/2008/06/migrating-to-optimal-rbac-with-minimal.html

Dana Zhang's Research Blog. A PhD candidate's random collection of research notes that may or may not be related to data mining and role based access control. Monday, June 23, 2008. Migrating to Optimal RBAC with Minimal Perturbation. Jaideep Vaidya, Vijayalakshmi Atluri and Qi Guo. Migrating to Optimal RBAC with Minimal Perturbation. In SACMAT’08: Proceedings of the thirteenth ACM symposium on Access control models and technologies, Estes Park, Colorado, June 2008. To measure the similarity of between t...

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Evaluating E-Discovery

William Webber's E-Discovery Consulting Blog. Sampling with zero intent. March 15th, 2018. A zero intent sample is a sample which will only satisfy our validation goal if no positive examples are found in it. If we have a population (in e-discovery, typically a document set) where one in R instances are positive (one in R documents relevant), and we only want a one in Q probability of sampling a positive instance, then our sample size can be no more than R / Q. Read the rest of this entry ». Of estimatin...

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