The Chagas disease is a potentially life-threatening illness caused by the protozoan parasite, Trypanosoma cruzi. Visual detection of such parasite through microscopic inspection is a tedious and time-consuming task. In this paper, we provide an AdaBoost learning solution to the task of Chagas parasite detection in blood images. We give details of the algorithm and our experimental setup. With ...

In this work, we present a novel classification method for geoinformatics tasks, based on a regularized version of the AdaBoost algorithm implemented in the GIS GRASS. AdaBoost is a machine learning classification technique based on a weighted combination of different realizations of a same base model. AdaBoost calls a given base learning algorithm iteratively in a series of runs: at each run, ...

Asymmetric classification problems are characterized by class imbalance or unequal costs for different types of misclassifications. One of the main cited weaknesses of AdaBoost is its perceived inability to handle asymmetric problems. As a result, a multitude of asymmetric versions of AdaBoost have been proposed, mainly as heuristic modifications to the original algorithm. In this paper we chal...

There has long been a chasm between theoretical models of machine learning and practical machine learning algorithms. For instance, empirically successful algorithms such as C4:5 and backpropagation have not met the criteria of the PAC model and its variants. Conversely, the algorithms suggested by computational learning theory are usually too limited in various ways to nd wide application. The...

Boosting is a general method for improving the accuracy of any given learning algorithm. Focusing primarily on the AdaBoost algorithm , we brieey survey theoretical work on boosting including analyses of AdaBoost's training error and generalization error, connections between boosting and game theory, methods of estimating probabilities using boosting, and extensions of AdaBoost for multiclass c...

There are two main approaches to the problem of gender classification, Support Vector Machines (SVMs) and Adaboost learning methods, of which SVMs are better in correct rate but are more computation intensive while Adaboost ones are much faster with slightly worse performance. For possible real-time applications the Adaboost method seems a better choice. However, the existing Adaboost algorithm...

Boosting is a general method for improving the accuracy of any given learning algorithm. Focusing primarily on the AdaBoost algorithm , we brieey survey theoretical work on boosting including analyses of AdaBoost's training error and generalization error, connections between boosting and game theory, methods of estimating probabilities using boosting, and extensions of AdaBoost for multiclass c...

We had previously introduced Learn, inspired in part by the ensemble based AdaBoost algorithm, for incrementally learning from new data, including new concept classes, without forgetting what had been previously learned. In this effort, we compare the incremental learning performance of Learn and AdaBoost under several combination schemes, including their native, weighted majority voting. We sh...

Boosting is a general method for improving the accuracy of any given learning algorithm. Focusing primarily on the AdaBoost algorithm , we brieey survey theoretical work on boosting including analyses of AdaBoost's training error and generalization error, connections between boosting and game theory, methods of estimating probabilities using boosting, and extensions of AdaBoost for multiclass c...

This article will give a general overview of boosting and in particular AdaBoost. AdaBoost is the most popular boosting algorithm. It has been shown to have very interesting properties such as low generalization error as well as an exponentially decreasing bound on the training error. The article will also give a short introduction to learning algorithms.