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Real life datasets often suffer from the problem of class imbalance, which thwarts supervised learning process. In such data sets examples of positive (minority) class are significantly less than those of negative (majority) class leading to severe class imbalance. Constructing high quality classifiers for such imbalanced training data sets is one of the major challenges in machine learning, since...
This paper presents a new algorithm based on boosting for interactive object retrieval in images. Recent works propose ”online boosting” algorithms where weak classifier sets are iteratively trained from data. These algorithms are proposed for visual tracking in videos, and are not well adapted to ”online boosting” for interactive retrieval. We propose in this paper to iteratively build weak classifiers...
We investigate AdaBoost and bipartite version of RankBoost abilities to minimize AUC and its application for score level fusion in multimodal biometric systems. To do this, we customize two methods of weak learner training. Empirical results show comparable AUC for AdaBoost and RankBoost.B which previously was addressed theoretically. We demonstrate exhaustive results among state of the art classifiers...
Similarity metrics that are learned from labeled training data can be advantageous in terms of performance and/or efficiency. These learned metrics can then be used in conjunction with a nearest neighbor classifier, or can be plugged in as kernels to an SVM. For the task of categorization two scenarios have thus far been explored. The first is to train a single “monolithic” similarity metric that...
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