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Sapevate che l'iPhone 5 in carica potrebbe darti una scossa mortale 

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Bypass Apple Icloud Activation Lock On Iphone Ipads Ipod With SSH Trick(no Gsm) - Phones - Nairaland

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In iOS 7, Apple added a anti-theft feature called Activation Lock to the Find My iPhone service, which ties your iPhone, iPad or iPod touch with your Apple ID so that your lost or stolen iOS device, cannot be used or restored without the login credentials. The anti-lock feature has been quite effective, as law enforcement officials in the U.S. reported that the feature has helped in significantly reducing iPhone theft in major cities. We have come across several cases where users who have purchased used iPhones, and have been stuck with an usable iPhone because the previous owner has forgotten to turn off the Find my iPhone feature before shipping the device. well,if you are stucked in this icloud activation lock ,you can still remove it using this method but the gsm function will be disabled so only wifi and other features will work.this can be done within 10minute. you will need the following  (1) ssh jar  download ssh jar here. pls copy and paste the link in your browser an...
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Cost-Sensitive Learning (slides)

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This course material presents approaches for the consideration of misclassification costs in supervised learning. The baseline method is the one for which we do not take into account the costs. Two issues are studied : the metric used for the evaluation of the classifier when a misclassification cost matrix is provided i.e. the expected cost of misclassification (ECM); some approaches which enable to guide the machine learning algorithm towards the minimization of the ECM. Keywords : cost matrix, misclassification, expected cost of misclassification, bagging, metacost, multicost Slides : Cost Sensitive Learning References : Tanagra Tutorial, " Cost-senstive learning - Comparison of tools ", March 2009. Tanagra Tutorial, " Cost-sensitive decision tree ", November 2008.
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New features for PCA in Tanagra

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Principal Component Analysis (PCA)  is a very popular dimension reduction technique. The aim is to produce a few number of factors which summarizes as better as possible the amount of information in the data. The factors are linear combinations of the original variables. From a certain point a view, PCA can be seen as a compression technique. The determination of the appropriate number of factors is a difficult problem in PCA. Various approaches are possible, it does not really exist a state-of-art method. The only way to proceed is to try different approaches in order to obtain a clear indication about the good solution. We had shown how to program them under R in a recent paper . These techniques are now incorporated into Tanagra 1.4.45 . We have also added the KMO index (Measure of Sampling Adequacy – MSA) and the Bartlett's test of sphericity  in the Principal Component Analysis tool. In this tutorial, we present these new features incorporated into Tanagra on a realistic ...
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