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Nowadays the exponential growth of generation of textual documents and the emergent need to structure them increase the attention to the automated classification of documents into predefined categories. There is wide range of supervised learning algorithms that deal with text classification. This paper deals with an approach for building a machine learning system in R that uses K-Nearest Neighbors...
In the standard EM-based semi-supervised text classification, the classification performance is not well when the initial labeled samples are a few. How to improve the performance is an important issue. In view of this, a semi-supervised method based on incremental EM algorithm is proposed. This method makes full use of the useful information of intermediate classifier. On the one hand, this method...
Question classification plays a crucial important role in the question answering system. Recent research on question classification for open-domain mostly concentrates on using machine learning methods to resolve the special kind of text classification. This paper presents our research about Chinese question classification using machine learning method and gives our approach based on SVM and semantic...
The goal of active learning is to select the most informative examples for manual labeling in order to reduce the effort involved in acquiring labeled examples, which is very important for large-scale text classification. However, most of the previous studies in active learning have focused on selecting a single unlabeled example at a time which could be inefficient since the model has to be retrained...
Most semi-supervised learning methods assume there are a number of labeled data available in order to learn a classifier which then exploits a large set of unlabeled data. However, for some applications, there are only extremely spare labeled examples attainable (say, one example per category). In this case, these semi-supervised learning methods can not work. In this paper, a new method for seeking...
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