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    <p>First off, it would be nice to if you could link/name the paper you are referring to.</p> <p><a href="https://en.wikipedia.org/wiki/AdaBoost" rel="nofollow">AdaBoost</a> is a meta classification algorithm, as it combines multiple classifiers called weak learners. These weak learners are often really simple, e.g. they only classify the data based on one feature, and perform slightly better than random.</p> <p>In image classification, AdaBoost will use as input a data set of images (with corresponding labels depicting to which class each sample belongs) and a set of weak learners. AdaBoost will then find the weak learner with the lowest error rate (i.e. best results) on the data. All correctly classified data samples are now given a lower weight as they are now less important, while the miss-classified samples are given a higher weight. AdaBoost will now start a new round and selects the best weak learner based on the newly weighted data. In other words, it will find a new weak learner which is better at classifying the samples which the previously selected weak learners were not able to classify.</p> <p>The algorithm will continue with selecting these weak learners for a specified amount of iterations. The output consists of the group of selected weak learners. The learned classifier can now classify new images based on a majority vote of each weak classifier in the group (often the weak classifiers themselves are also weighted based on their achieved error rate).</p> <p>You might want to take a look at software that have already implemented AdaBoost, like <a href="http://www.cs.waikato.ac.nz/ml/weka/" rel="nofollow">WEKA</a> or the the computer vision orientated <a href="http://opencv.willowgarage.com/wiki/" rel="nofollow">OpenCV</a>.</p>
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    1. POAdaBoost Input and Output?
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    1. COAnd what is the output of the classifier? For instance, Neural Network that utilize sigmoid function and trained to recognizes 2 classes (0 and 1) outputs the value between 0 and 1 that shows relative probability that detected context belongs to one of these classes. But what would be output from Adaboost algorithm for the same example?
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    2. CO@supermus AdaBoost can be used for classification or regression. The output in the classification case is one class from a fixed set of classes. In the regression case AdaBoost can output any number.
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