{"action":"closed","issue":{"url":"https://api.github.com/repos/LevinJ/Pedestrian-Detection-Project/issues/2","labels_url":"https://api.github.com/repos/LevinJ/Pedestrian-Detection-Project/issues/2/labels{/name}","comments_url":"https://api.github.com/repos/LevinJ/Pedestrian-Detection-Project/issues/2/comments","events_url":"https://api.github.com/repos/LevinJ/Pedestrian-Detection-Project/issues/2/events","html_url":"https://github.com/LevinJ/Pedestrian-Detection-Project/issues/2","id":54139657,"number":2,"title":"Need to addd more traces for adaboost training processs to help figure out why dct method does not work","user":{"login":"LevinJ","id":1629014,"avatar_url":"https://avatars.githubusercontent.com/u/1629014?v=3","gravatar_id":"","url":"https://api.github.com/users/LevinJ","html_url":"https://github.com/LevinJ","followers_url":"https://api.github.com/users/LevinJ/followers","following_url":"https://api.github.com/users/LevinJ/following{/other_user}","gists_url":"https://api.github.com/users/LevinJ/gists{/gist_id}","starred_url":"https://api.github.com/users/LevinJ/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/LevinJ/subscriptions","organizations_url":"https://api.github.com/users/LevinJ/orgs","repos_url":"https://api.github.com/users/LevinJ/repos","events_url":"https://api.github.com/users/LevinJ/events{/privacy}","received_events_url":"https://api.github.com/users/LevinJ/received_events","type":"User","site_admin":false},"labels":[],"state":"closed","locked":false,"assignee":null,"milestone":null,"comments":1,"created_at":"2015-01-13T01:35:01Z","updated_at":"2015-01-14T09:31:27Z","closed_at":"2015-01-14T09:31:27Z","body":"Specifically. For each iteration in adaboost training:\r\n1. which feature is selected as the weak classifier\r\n2. the weighted sample error rate for the weak classifier\r\n3. the updated sample weight\r\n4. the overall error rate for the training samples\r\n5. the margin changes:the\r\ndifference between the weighted fraction of the weak classifiers predicting the cor-\r\nrect label and the weighted fraction predicting the incorrect label\r\n\r\nand at the end of the training,  for all the features selected as weak classifier:\r\n1. which channel they are from\r\n2 their position\r\n3. their weights in the final strong classifier\r\n\r\n"}}