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Improves reliability of syncing mail, contacts and notes with Exchange accountsįor information on the security content of Apple software updates, please visit this website: iOS 11.4 This update:įixes an issue that prevented some users from viewing the last known location of their AirPods in Find My iPhone IOS 11.4.1 includes bug fixes and improves the security of your iPhone or iPad. ![]() ![]() One or multiple pictures may be opened at the same time (Multiple window usage must be activated using the "View" menu, entry "Allow multiple").Īnother feature is to browse the pictures of a folder (Menu "View", subentry "Folder"). A set of editing tools is as well integrated, like line drawing, circle drawing, text adding as a set of filters (negative, blur, sharpen). Gives you the ability to resize your pictures to suit your needs.Alternate Pic View is a simple picture viewer and manipulator that allows you to view and edit images. 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Alternate Pic View: Take your images to the next level with this handy, convenient, and powerful photo editor ![]() ![]() ![]() Net12 <- grnn.fit( x = X1, y = Y1, sigma = test1 $ best $ sigma) I <- sample(seq(length( Y)), length( Y) / 2) In this particular example, it is clearly shown that there is roughly a 4% – 5% improvement in the AUC statistic for both training and hold-out samples through the use of monotonic binning and WoE transformations.ĭf1 <- read.table( "credit_count.txt ", header = T, sep = ", ")ĭf2 <- df1 For the second GRNN with WoE-transformed predictors, the AUC for the training sample is 0.72 and the AUC for the hold-out sample is 0.69.For the first GRNN with untransformed raw predictors, the AUC for the training sample is 0.69 and the AUC for the hold-out sample is 0.66.sigma, was chosen through the random search and happened to be 2.198381 for both GRNNs. ![]() one as the training sample and another as the hold-out sample. While talk is cheap, I would use the example below to show that using the monotonic binning algorithm to pre-process predictors in a GRNN is actually able to alleviate the over-fitting and to improve the prediction accuracy for the hold-out sample.įirst of all, the whole dataset was split into half, e.g. Yap::logl(y_pred = yap::pnn.predict(n2, X), y_true = yap::dummies(Y))Ī major criticism on the binning algorithm as well as on the WoE transformation is that the use of binned predictors will decrease the model predictive power due to the loss of data granularity after the WoE transformation. N2 <- yap::pnn.fit(X, Y, sigma = parm$best$sigma) ![]() Yap::logl(y_pred = predict(m1, newdata = X, type = "prob"), y_true = yap::dummies(Y)) # FIT A MULTINOMIAL REGRESSION AS A BENCHMARK # In this particular example, PNN even performed slightly better in terms of the cross-entropy for a separate testing dataset. As shown below, both approaches delivered very comparable predictive performance. Similar to GRNN, PNN shares same benefits of instantaneous training, simple structure, and global convergence.īelow is a demonstration showing how to use the YAP package and a comparison between the multinomial regression and the PNN. By the end of 2019, I finally managed to wrap up my third R package YAP ( ) that implements the Probabilistic Neural Network (Specht, 1990) for the N-category pattern recognition with N > 2. ![]() ![]() ![]() MindManager lets your brain work with information the way it was designed to - fast & dynamically - so you don’t lose a single brilliant insight or critical detail. ![]() Rigid, linear tools can leave your best thinking on the table. Make your maps communicate exactly what you need, with new capabilities like Timeline layout, more powerful filtering, easier navigation and walk-through mode. With improved HTML5 export, you can easily share your maps and create alignment with just about anyone - on your team, in your org and beyond. 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If you are using an earlier version of Windows, you should probably upgrade to Windows 10 without worrying about performance or compatibility issues. Overall, the user experience of Windows 10 is much better than Windows 8 and even Windows 7 (provided you have enough hardware resources available). Although it provides basic protection, it’s much better than having nothing at all. Windows 10 comes with a preinstalled antivirus and a firewall called Microsoft Defender. It has better multitasking capabilities and file management features, allowing users to work simultaneously on different apps without noticeable performance lag. The boot time is much shorter, and the start menu is faster (and not annoyingly full screen). Windows 10 is much better than Windows 8 in terms of speed, security and feature set. Overall, Windows 8 had a negative impact on user experience, as it seemed half baked compared to Windows 7. 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