Semantic Scholar uses AI to extract papers important to this topic.
We propose a hierarchical attention network for document classification. Our model has two distinctive characteristics: (i) it… Expand Many machine learning algorithms require the input to be represented as a fixed-length feature vector. When it comes to texts… Expand Machine learning approaches to multi-label document classification have to date largely relied on discriminative modeling… Expand With the increasing availability of electronic documents and the rapid growth of the World Wide Web, the task of automatic… Expand A major assumption in many machine learning and data mining algorithms is that the training and future data must be in the same… Expand This paper shows that the accuracy of learned text classifiers can be improved by augmenting a small number of labeled training… Expand We present a novel sequential clustering algorithm which is motivated by the Information Bottleneck (IB) method. In contrast to… Expand We implemented versions of the SVM appropriate for one-class classification in the context of information retrieval. The… Expand From the publisher: This is the first comprehensive introduction to Support Vector Machines (SVMs), a new generation learning… Expand In this paper we present a simple linear-time centroid-based document classification algorithm, that despite its simplicity and… Expand