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Learning to rank is becoming more and more popular in machine learning and information retrieval field. However, like many other supervised approaches, one of the main problems with learning to rank is lack of labeled data. Recently, there have been attempts to address the challenges in active sampling for learning to rank. But none of these methods take into consideration the differences between...
Learning to rank is one of the hottest topics in information retrieval (IR) field. Ranking SVM (RSVM) is a typical method of learning to rank. But this approach is time consuming, which decreases its applicability in real-world IR applications, which involves a large amount of computation, because it requires increasing the complexity from n to O(n2). This paper analyzes the characteristics of the...
We present a new method for classification with structured latent variables. Our model is formulated using the max-margin formalism in the discriminative learning literature. We propose an efficient learning algorithm based on the cutting plane method and decomposed dual optimization. We apply our model to the problem of recognizing human actions from video sequences, where we model a human action...
Classification of data with imbalanced class distribution has posed a significant drawback of the performance attainable by most standard classifier learning algorithms, which assume a relatively balanced class distribution and equal misclassification costs. This learning difficulty attracts a lot of research interests. Most efforts concentrate on bi-class problems. However, bi-class is not the only...
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