I'm comparing different machine learning for classifying sensor data and I need their complexity to select the most efficient. Which are the Big-O notation for the following algorithms (I add what I found):
- SVM (linear and all vs rest)- O(n^3)
- Random Forest (CART) - O (M(mn logn)) M-number of trees m-number of attributes and n-number of samples.
- Linear Discriminative Analysis (Fisher's) - O(2^n)
- Multilayer perceptron (learning-backpropagation and classification feed-forward)
Are they correct? Any source to find their Big-O notation?
I'm asking: 1) Are the Big O notation correct? 2) If not, which are the correct? (approximated)