In order to understand AdaBoost’s dynamics, especially its ability to maximize margins, we derive an associated simplified nonlinear iterated map and analyze its behavior in low-dimensional cases. We find stable cycles for these cases, which can explicitly be used to solve for AdaBoost’s output. By considering AdaBoost as a dynamical system, we are able to prove Rätsch and Warmuth’s conjecture ...

AdaBoost is an excellent committee-based tool for classification. However, its effectiveness and efficiency in multiclass categorization face the challenges from methods based on support vector machine SVM , neural networks NN , naı̈ve Bayes, and k-nearest neighbor kNN . This paper uses a novel multi-class AdaBoost algorithm to avoid reducing the multi-class classification problem to multiple tw...

We describe an algorithm capable of detecting multiple object instances within a scene in the presence of changes in object viewpoint. Our approach consists of first calculating frequency vectors for discrete feature vector clusters (visual words) within a sliding window as a representation of the image patch. We then classify each patch using an AdaBoost classifier whose weak classifier simply...

The Support Vector Machine (SVM) is a powerful classification technique that has been used extensively in the field of medical imaging. A model based on SVM with Gaussian RBF kernel is proposed here for the automatic detection of brain tumor from MRI images. Various textural characteristics of the MRI images of human brain are extracted to construct a feature set. These features sets are then u...

Problem statement: Nowadays, the Internet plays an important role in communication between people. To ensure a secure communication between two parties, we need a security system to detect the attacks very effectively. Network intrusion detection serves as a major system to work with other security system to protect the computer networks. Approach: In this article, an Adaboost algorithm for net...

Automatic image annotation consists on automatically labeling images, or image regions, with a pre-defined set of keywords, which are regarded as descriptors of the high-level semantics of the image. In supervised learning, a set of previously annotated images is required to train a classifier. Annotating a large quantity of images by hand is a tedious and time consuming process; so an alternat...

The main idea is to formulate the tracking problem as a binary classification task and to achieve robustness by continuously updating the current classifier of the target object with respect to the current surrounding background. For this purpose we use an on-line AdaBoost feature selection algorithm [1] for tracking. The distinct advantage of the method is its capability of updating a model (c...

The Gaussian mixture models (GMM) has proved to be an effective probabilistic model for speaker verification, and has been widely used in most of state-of-the-art systems. In this paper, we introduce a new method for the task: that using AdaBoost learning based on the GMM. The motivation is the following: While a GMM linearly combines a number of Gaussian models according to a set of mixing wei...

The main contribution of this paper is the use of an AdaBoost-based learning algorithm which builds a strong classifier from a set of weak classifiers associated with level curves in the nasal region of 3D faces. Its main application is person authentication. The basic idea is to represent nasal surfaces using indexed collections of level curves, and to compare shapes of noses by comparing the ...