We invented Reduced Error Logistic Regression (see Press Release for our US patent 8,032,473), and this also now includes an international patent application (PCT/US14/46060). Reduced Error Logistic Regression (RELR) is a completely automated and very general machine learning method that avoids traditional problems related to dimensionality and multicollinearity. RELR is the subject of a book by Daniel M. Rice titled Calculus of Thought: Neuromorphic Logistic Regression in Cognitive Machines published by Elsevier: Academic Press with a 2014 copyright. RELR is currently used daily to help profile likely customers and thus target media buying in a very large number of well-known consumer brands.
Key News Events
May 29, 2015. We thought that we would be ready to launch our SkyRELR web app for public usage by the end of this month, as we are very close. However, we are still in the process of getting expert level opinion in terms of Beta tests. Stay tuned as it does finally look very close and could be as early as by the end of next month and definitely will be this summer. This web app will include all the features that have worked for years to reduce error in logistic regression like our error modeling, as well as some new features like the ability to handle outliers automatically simply by converting ratio and interval level predictors to ordinal variables. We will make a formal announcement in industry publications when we finally launch. For more details, please contact us at email@example.com.
July 10, 2014. An international patent application (PCT/US14/46060) was submitted today "Extensions to the Generalized Reduced Error Logistic Regression Method" that includes six important extensions that rest upon the originally patented RELR method, but which substantially improve the originally patented RELR machine learning methods in terms of even much more stable and accurate performance in specific areas, along with extensions for sequential and causal machine learning based upon RELR.
July 3, 2014. The client side to our new SkyRELR.com website is fully functioning. Please visit http://www.SkyRELR.com for the regular blog that we will have about this product and associated RELR machine learning starting later in July.
January 28, 2014. Calculus of Thought is now fully released in both print and electronic versions and the companion website to the book is now also available at this link to the Elsevier website: http://booksite.elsevier.com/9780124104075/ This companion website contains a pdf document that lists a couple of small typing errata in the book and links to Excel Workbooks that run easy to understand toy model examples from the book that anyone is able to download even without purchasing the book.
November 15, 2013. Calculus of Thought is available as of today for its first day of sale out on Amazon.com and the Store.Elsevier.com websites and will be available at Barnes and Noble and other booksellers in the next few weeks. The companion website to the book which contains a few small errata and links to Excel Workbooks that run toy model examples in the book is still under construction, but should be complete within the next week or so.
November 1, 2013. Update: You can now "Look Inside" Calculus of Thought out on Amazon.com and read much of the preface and first chapter. Dan Rice will be presenting at Society for Neuroscience next week in San Diego and likely will have an advance copy of a printed book at that time.
January 11, 2013. Dan Rice is under contract with Elsevier for a book on RELR to be titled Calculus of Thought likely to be published later this year in the fall. The title is based upon Leibniz's idea that the goal of calculus is to develop a Calculus Ratiocinator or a computational machine that mimics human cognition but without the subjective biases of humans.
Oct 4, 2011. We were issued a patent today for our RELR technology by the US Patent Office. A full description of the significance of this patent is available at this link to our Patent Press Release Article.
August 4, 2011. A new case study is available from a completely independent researcher with no connections to Rice Analytics that is a comparison of RELR with Random Forests Logistic Regression, LASSO, LARS, Stepwise Regression, and Bayesian Networks and shows that RELR outperforms these other algorithms in classification accuracy by an average of 2-4%. Here is the link RELRCaseStudyAugust2011 to the page on our website where it can be viewed.
September 9, 2009. St. Louis, MO (USA) - Our new executive white paper written by Dan Rice and entitled "Breiman's Quiet Scandal: Stepwise Logistic Regression and RELR" was in the Publications section of the online industry newletter KDnuggets.com on August 27, 2009 (issue 09:n16). This item had the Most Clicks by Subscribers and was the 2nd Most Viewed item overall of 41 items that were published that week. This article written in "plain business English" for executives reviews the major difficulties with Stepwise Logistic Regression that were pointed out by the late statistician Leo Breiman. This article also reviews evidence that our RELR method may be a solution to these problems. The complete white paper can be downloaded by clicking this link to the Executive White Paper page of this website.
June 15, 2009. St. Louis, MO (USA) - Dan Rice gave an invited address last week at the 2009 Classification Society Annual Conference from June 11-13 at the Washington University Medical School. This conference brought together roughly a hundred experts from major universities and businesses in the areas of machine learning, choice modeling, and classification research. This conference was truly international in scope and had attendees from many industrialized countries. However, the relatively small size of this conference compared to JSM allowed for an extended discussion between attendees over the course of several days. The title of this talk was "Reduced Error Logistic Regression". This talk can be downloaded from the Papers and Presentations page of this website.
August 6, 2008. Denver, CO (USA) - Dan Rice spoke today at the Data Mining and Machine Learning Session of the 2008 Joint Statistical Meetings in Denver, Colorado. This session was chaired by Bill Heavlin of Google Inc. and had good speakers from the United States Army, Medical University of China, University of Alabama, Bell Labs, and the University of California at Berkeley. This session was extremely well attended with a standing-room-only crowd. This standing-room-only crowd and the lively discussions prompted Bill Heavlin to say that this session "was the best session at the conference". The title of Rice's talk was "Generalized Reduced Error Logistic Regression Machine". In this presentation, Rice provided evidence that Reduced Error Logistic Regression is able to reduce error significantly compared to Penalized Logistic Regression, Step-Wise Logistic Regression and four other standard methods. A full article coinciding with this talk and published in JSM 2008 Proceedings can be downloaded from the Papers and Presentations page of this website. The Joint Statistical Meetings is one of the largest gatherings of statisticians in the world. Approximately 5000 people attended this conference in Denver this summer.