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The use of unsupervised learning algorithms (e.g. K-means and hierarchical clustering) is strongly recommended for the analysis of social media data. In the same vein, supervised learning algorithms (e.g. classification and regression) can be applied to bigger data sets, given that a sufficiently large training set is available. The main advantage of supervised learning is its ability to identify a relationship between different variables. However, it is important to note that supervised methods usually require large amounts of data on both sides of the relationship. The use of other types of algorithms (e.g. graph based community detection algorithms, co-authorship networks, and item-based topic models) have been successfully applied to social media data in order to uncover latent features in the form of communities or topics (e.g. items).
PCSX2 is a complete emulator, which means it needs a lot of RAM to work properly. In the best case scenario, you should have 8GB of RAM. However, as of now, the emulator will run only with 4GB or less. Note that the emulator will not run with less than 2GB, even if you have enabled the boot with low memory option.
The project is still in development and there are many bugs. In the best case scenario, the emulator performs at 30fps on even the most demanding games. It's recommended you use a monitor's native refresh rate or a higher screen resolution when playing. However, if you want to use some effects, particularly the ones currently available on PCSX2 and see them in action, you need to configure the emulator.
Some applications, such as Java, require that the application directories and libraries are updated to a newer version before the user can update. However, there are some applications that are not explicitly API-specific, such as the X Window System, the X server source code, are automatically updated as required.
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