From: How much do social connections matter in fundraising outcomes?
MLP | Decision tree | LR | Random forest |
---|---|---|---|
Feed data to input layer Set connection factor w, offset b in hidden layer Use Sigmoid function as activation function Output classification results |
Compute information gain for each variable Select the variable with largest information gain as the root node Repeat above steps until tree is built |
Construct prediction function, using Eq. (3) Construct loss function J(θ) Using gradient descent to find smallest J(θ) |
Random sampling Split nodes Repeat step 2 until no more splits Repeat steps 1 to 3 to build decision trees to form a random forest |
Bayesian inference | KNN | AdaBoost | SVM |
Set a priori probability Set conditional probability from the given information Transform a priori probability into posterior probability with the information |
Randomly select n nodes as classification centres Compute distance from each node to the centres Update centres Repeat steps 1 to 3 until convergence |
Initialize weight distribution D_{1} Train weak classifier Combine the trained weak classifiers into a strong classifier |
Find number of classifiers Apply kernel function Train data to obtain hyperplane Repeat steps 1 to 3 until convergence |