In this lecture we will continue our discussion of the Adaboost algorithm and derive a bound on the generalization error. We saw last time that the training error decreases exponentially with respect to the number of rounds T . However, we also want to see the performance of this algorithm on new test data. Today we will show why the Adaboost algorithm generalizes so well and why it avoids over...

The use of modified Real Adaboost ensembles by applying weighted emphasis on erroneous and critical (near the classification boundary) has been shown to lead to improved designs, both in performance and in ensemble sizes. In this paper, we propose to take advantage of the diversity among different weighted combination to build committees of modified Real Adaboost designs. Experiments show that ...

Detecting text regions in natural scenes is an important part of computer vision. We propose a novel text detection algorithm that extracts six different classes features of text, and uses Modest AdaBoost with multi-scale sequential search. Experiments show that our algorithm can detect text regions with a f= 0.70, from the ICDAR 2003 datasets which include images with text of various fonts, si...

In this paper is proposed a hybrid face detector that combines the high processing speed of an Asymmetrical Adaboost Cascade Detector with the high detection rate of a Wavelet Bayesian Detector. This integration is achieved by incorporating this last detector in the middle stages of the cascade detector. Results of the application of the proposed detector to a standard face detection database a...

A common approach to pattern recognition and object detection is to use a statistical classifier. Widely used method is AdaBoost or its modifications which yields outstanding results in certain tasks like face detection. The aim of this work was to build real-time system for detection of dogs for surveillance purposes. The author of this paper thus explored the possibility that the AdaBoost bas...

We have recently proposed an extension of ADABOOST to regression that uses the median of the base regressors as the final regressor. In this paper we extend theoretical results obtained for ADABOOST to median boosting and to its localized variant. First, we extend recent results on efficient margin maximizing to show that the algorithm can converge to the maximum achievable margin within a pres...

AdaBoost has been theoretically and empirically proved to be a very successful ensemble learning algorithm, which iteratively generates a set of diverse weak learners and combines their outputs using the weighted majority voting rule as the final decision. However, in some cases, AdaBoost leads to overfitting especially for mislabeled noisy training examples, resulting in both its degraded gene...

Discuss approaches to combine techniques used by ensemble learning methods. Randomness which is used by Bagging and Random Forests is introduced into Adaboost to get robust performance under noisy situation. Declare that when the randomness introduced into AdaBoost equals to 100, the proposed algorithm turns out to be a Random Forests with weight update technique. Approaches are discussed to im...

In this letter, we present useful features accounting for pronunciation prominence and propose a classification technique for prominence detection. A set of phone-specific features are extracted based on a forced alignment of the test pronunciation provided by a speech recognition system. These features are then applied to the traditional classifiers such as the support vector machine (SVM), ar...

Article history: Received 15 September 2010 Available online 22 November 2011 Communicated by F. Roli