Machine Learning Review
y = f(x)
output, prediction function, training/testing example
test image -> image features -> learned model -> prediction
raw pixels, histograms, gist descriptors
Decision Tree: Determining which attribute is best
Entropy(E) is the minimum number of bits needed represent the examples according to their class labels
There is no perfect way of labelling data, therefore there is really no perfect IDS dataset.
(flag = S0, service = http),(flag = S0, service = http) -> (flag = S0, service = http)[0.6, 2s]
