[MXML-9-01] AdaBoost [1/4] - Overview, Boosting, Algorithm for Binary Classification скачать в хорошем качестве

[MXML-9-01] AdaBoost [1/4] - Overview, Boosting, Algorithm for Binary Classification

** This video was produced in Korean and translated into English. And the voice is generated by TTS. ** Dubbing: [ English ] [ 한국어 ] This is part 1 of a series on AdaBoost algorithm called Adaptive Boosting, which is the 9th module of machine learning. AdaBoost is a boosting algorithm proposed by Yoav Freund of AT&T Bell-labs in 1995. Using AdaBoost improves accuracy. This is an ensemble learning method that trains multiple models from sampled data and aggregates the results. In general, boosting is an algorithm that gradually strengthens weak learner models. And the prediction step synthesizes the results of multiple trained weak learner models. AdaBoost is an algorithm that learns from increasingly more misclassified data points. Let's take a look at the full table of contents of a series on AdaBoost. Chapter 1 and Chapter 2 provide an overview of AdaBoost. In Chapter 3, we will look at the AdaBoost algorithm for binary classification. Chapter 4 deals with the multiclass classification problem. The SAMME algorithm is used for multiclass classification. And in Chapter 6, we're going to look at AdaBoost's regression algorithm. In this video, we will look at the contents of chapters 1 and 2. #AdaptiveBoosting #AdaBoost #Boosting #YoavFreund