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Personal Info Affiliations Subscriptions My Papers My Briefcase Sign out Advanced Search Abstract https://ssrn.com/abstract=2333736 Download This running out of memory windows 10 Paper Open PDF in Browser | Share | Email | Add steelray project viewer out of memory error to MyBriefcase | Purchase Bound Hard Copy Facebook | Twitter | CiteULike | Permalink Using the URL how to fix out of memory error in java or DOI link below will ensure access to this page indefinitely Based on your IP address, your paper is being delivered by: New York, USA Processing request. https://support.microsoft.com/en-us/kb/126962 Illinois, USA Processing request. Brussels, Belgium Processing request. Seoul, Korea Processing request. California, USA Processing request. If you have any problems downloading this paper,please click on another Download Location above, or view our FAQ File name: SSRN-id2333736. ; Size: 268K You will receive a perfect bound, 8.5 x 11 inch, black and white printed copy of http://ssrn.com/abstract=2333736 this PDF document with a glossy color cover. Currently shipping to U.S. addresses only. Your order will ship within 3 business days. For more details, view our FAQ. Quantity: Total Price = $9.99 plus shipping (U.S. Only) If you have any problems with this purchase, please contact us for assistance by email: Support@SSRN.com or by phone: 877-SSRNHelp (877 777 6435) in the United States, or +1 585 442 8170 outside of the United States. We are open Monday through Friday between the hours of 8:30AM and 6:00PM, United States Eastern. Taking the Error Out of 'Error Cost' Analysis: What's Wrong with Antitrust's Right Jonathan B. Baker American University - Washington College of Law July 19, 2015 Antitrust Law Journal , Vol. 80, No. 1, 2015 American University, WCL Research Paper No. 2016-13 Abstract: This article catalogues a series of erroneous assumptions about the current competition policy environment made by today’s antitrust conservatives. These errors inappropriately tilt the application of a neutral economic tool, decision theory,
Random Forests?What does it mean? What's a typical value, if any? Why would it be higher or lower than a typical value?UpdateCancelAnswer Wiki5 Answers Manoj Awasthi, Machine learning newbie.Written 158w agoI will take an attempt https://www.quora.com/What-is-the-out-of-bag-error-in-Random-Forests to explain: Suppose our training data set is represented by T and suppose data set has M features (or attributes or variables).T = {(X1,y1), (X2,y2), ... (Xn, yn)} and Xi is input vector {xi1, xi2, http://link.springer.com/chapter/10.1007%2F978-1-4020-9338-8_31 ... xiM} and yi is the label (or output or class). summary of RF: Random Forests algorithm is a classifier based on primarily two methods - bagging and random subspace method. Suppose we decide to have out of S number of trees in our forest then we first create S datasets of "same size as original" created from random resampling of data in T with-replacement (n times for each dataset). This will result in {T1, T2, ... TS} datasets. Each of these is called a bootstrap dataset. Due to "with-replacement" every dataset Ti can have duplicate data records and Ti can be missing several data records from original datasets. out of memory This is called Bagging. Now, RF creates S trees and uses m (=sqrt(M) or =floor(lnM+1)) random subfeatures out of M possible features to create any tree. This is called random subspace method. So for each Ti bootstrap dataset you create a tree Ki. If you want to classify some input data D = {x1, x2, ..., xM} you let it pass through each tree and produce S outputs (one for each tree) which can be denoted by Y = {y1, y2, ..., ys}. Final prediction is a majority vote on this set. Out-of-bag error:After creating the classifiers (S trees), for each (Xi,yi) in the original training set i.e. T, select all Tk which does not include (Xi,yi). This subset, pay attention, is a set of boostrap datasets which does not contain a particular record from the original dataset. This set is called out-of-bag examples. There are n such subsets (one for each data record in original dataset T). OOB classifier is the aggregation of votes ONLY over Tk such that it does not contain (xi,yi). Out-of-bag estimate for the generalization error is the error rate of the out-of-bag classifier on the training set (compare it with known yi's).Why is it important?The study of error estimates for bagged classifiers in Breiman
ChapterRethinking Popper Volume 272 of the series Boston Studies in The Philosophy of Science pp 417-423Out of Error: Further Essays on Critical RationalismDavid Miller Buy this eBook * Final gross prices may vary according to local VAT. Get Access In his new book, Miller returns to his central philosophical interest — to critical rationalism. Readers who are familiar with his previous book Critical Rationalism. A Restatement and Defence (Open Court 1994) know that Miller there reaffirms and further develops Popper's falsificationism and considers it not just a methodological issue relevant to science but a philosophical issue of rationality. In what new directions does Out of Error take us, given the fact that Critical Rationalism presents a pretty comprehensive account of the most important problems of Popper's methodology, including a systematic enumeration of objections voiced by his critics over the years, followed by their elimination? In this review, I will argue that readers will not be disappointed; Miller both provides new insights to the problems he dealt with before and addresses new problems, especially problems concerning applied science, the demarcation criterion, the use of Popper's rationalism against the fashionable postmodern currents, and the employment of paraconsistent logic in falsificationism. The book can be divided into three main parts: chapters 1, 14 were written in memoriam; in the second part (chapters 2–7) Miller carries out a philosophical investigation of critical rationalism; the third part (chapters 8–13) is more technical and deals with various logical aspects of critical rationalism. I will focus on and discuss mainly the problems of the first part of Out of Error. Page %P Close Plain text Look Inside Chapter Metrics Provided by Bookmetrix Reference tools Export citation EndNote (.ENW) JabRef (.BIB) Mendeley (.BIB) Papers (.RIS) Zotero (.RIS) BibTeX (.BIB) Add to Papers Other actions About this Book Reprints and Permissions Share Share this content on Facebook Share this content on Twitter Share this content on LinkedIn Supplementary Material (0) References (0) About this Chapter Title Out of Error: Further Essays on Critical Rationalism Book Title Rethinking Popper