[Retracted] Motivation Analysis of Technological Startups Business Models Based on Intelligent Data Mining and Analysis

Algorithm 1

The AdaBoost algorithm.

(a)	When the algorithm initializes the weights of the data set (first step), it is assumed that the data set initially has a uniform weight distribution, that is, each data sample has the same effect when training the first weak classifier;
(b)	When the algorithm calculates the training error of the weak classifier (the fifth step), it can be seen from formula (3) that the training error of the weak class is actually the sum of the weight values of all misclassified samples.
(c)	When the algorithm calculates the weight coefficient of the weak classifier (the sixth step). it can be seen from (4) that when , , the coefficient increases with the decrease of the training error. It can be seen that when the training error is greater than 50%, it indicates that the classification ability of the weak classifier is lower than random guessing. Therefore, the algorithm gives the weak classifier a negative weight to indicate that the weak classifier has a negative effect on the final classification result. A weak classifier with a smaller training error will get a greater weight, indicating that the classifier has a greater role in the final classification task.
(d)	When the algorithm updates the weight distribution of the training data (step 7), formula (6) shows that the data weight when training the next round of weak classifier is [18]