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The "ContentMimeDetection" page has been changed by Lukeliush: https://wiki.apache.org/tika/ContentMimeDetection?action=diff&rev1=20&rev2=21 We need to split the dataset into 3 chunks, training set, validation set and test set. - We convert the stream of bytes to the histogram with 255 bins each of which stores a count of occurances, [you can define your input with smaller number of histogram bins or the selected bins based on the domain knowledge, you can also apply a feature selection algorithm such as SOM, PCA or LCA when the features space is too huge (e.g. you might want to work with the entire bytes as the input), and you can also apply own custom functions on the input variables for the model to have non-linear effect, but to begin with, we need to understand our goal and the data, usually we need to visualize the data and we start with some simple algorithms to explore the data and then decide whether a more complex algorithm is needed]. + We convert the stream of bytes to the histogram with 255 bins each of which stores a count of occurances, [you can define your input with smaller number of histogram bins or the selected bins based on the domain knowledge, you can also apply a feature selection algorithm such as SOM, PCA or LCA when the features space is too huge (e.g. you might want to work with the entire bytes as the input), and you can also apply your own custom functions such as power or sqrt on the input variables for the model to have non-linear effect, there are also many other practical tricks to achieve training a good model, but most of them might require a bit understanding with the application domain (i.e. in this case, the file types to be classified); To begin with, we probably need to understand our goal and the data (domain if possible), usually we need to visualize the data and we start with some simple algorithms to explore the data and then decide whether a more complex algorithm or function is needed]. - Our training data have the 255 features each of which correspond to a byte, and each training example is labelled with an actual output indicating its class. + Our training data have the 255 features each of which corresponds to a byte, and each training example is labelled with an actual output indicating its class. '''Pre processing'''
