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The "ContentMimeDetection" page has been changed by Lukeliush: https://wiki.apache.org/tika/ContentMimeDetection?action=diff&rev1=19&rev2=20 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 or PCA, and you can 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, sometimes we need to visualize the data and usually we start with some simple algorithm 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 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]. - For simplicity, our training data have the following 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 correspond to a byte, and each training example is labelled with an actual output indicating its class. '''Pre processing''' @@ -175, +175 @@ The following lists all of the classes for this feature (tika\tika-core\src\main\java) + . org.apache.tika.detect.TrainedModelDetector (abstract) org.apache.tika.detect.ExampleNNModelDetector org.apache.tika.detect.TrainedModel (abstract) org.apache.tika.detect.NNTrainedModel - . org.apache.tika.detect.TrainedModelDetector (abstract) org.apache.tika.detect.ExampleNNModelDetector - org.apache.tika.detect.TrainedModel (abstract) org.apache.tika.detect.NNTrainedModel Example model file (tika\tika-core\src\main\resources)
