A new variant of AdaBoost is applied to a Minimum Bayes Risk discriminative training procedure that directly aims at reducing Word Error Rate for Automatic Speech Recognition. Both techniques try to improve the discriminative power of a classifier and we show that can be combined together to yield even better performance on a small vocabulary continuous speech recognition task. Our results also...

In this paper we propose to combine two powerful ideas, boosting and manifold learning. On the one hand, we improve ADABOOST by incorporating knowledge on the structure of the data into base classifier design and selection. On the other hand, we use ADABOOST’s efficient learning mechanism to significantly improve supervised and semi-supervised algorithms proposed in the context of manifold lear...

This paper studies boosting algorithms that make a single pass over a set of base classifiers. We first analyze a one-pass algorithm in the setting of boosting with diverse base classifiers. Our guarantee is the same as the best proved for any boosting algorithm, but our one-pass algorithm is much faster than previous approaches. We next exhibit a random source of examples for which a “picky” v...

With the explosive growth of music recordings, automatic classification of music emotion becomes one of the hot spots on research and engineering. Typical music emotion classification (MEC) approaches apply machine learning methods to train a classifier based on audio features. In addition to audio features, the MIDI and lyrics features of music also contain useful semantic information for pred...

Spam detection algorithms have been developed to train in a large enough set of labeled data and predict with a high accuracy of 95% if an email is spam or not. A problem that arises in this setting is that labeling examples is a costly process. It requires humans to read them one by one and classify them. Active learning is a learning approach developed to address this problem. It learns a sma...

We propose a novel approach to boosting weighted linear discriminant analysis (LDA) as a weak classifier. Combining Adaboost with LDA allows selecting the most relevant features for classification at each boosting iteration, thus benefiting from feature correlation. The advantages of this approach include the use of a smaller number of weak learners to achieve a low error rate, improved classif...

Face detection is an essential first step towards many advanced computer vision, biometrics recognition and multimedia applications, such as face tracking, face recognition, and video surveillance. In this paper, we proposed an FPGA hardware design with NoC (Network-on-Chip) architecture based on an AdaBoost face detection algorithm. The AdaBoost-based method is the state-of-the-art face detect...

Robust and accurate pupil detection is a prerequisite for gaze detection. Hence, we propose a new eye/pupil detection method for gaze detection on a large display. The novelty of our research can be summarized by the following four points. First, in order to overcome the performance limitations of conventional methods of eye detection, such as adaptive boosting (Adabo...

In this paper, an effective tweet classification system that fully supports the Turkish language has been developed. The proposed can be used for mining (classifying) recently published and publicly available tweets to find crisis’s most related useful gain situational awareness, which help in taking correct responses order prevent or at least decrease effect of such situations. A deep study wa...