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We present a novel approach to semi-supervised learning for text classification based on the higher-order co-occurrence paths of words. We name the proposed method as Semi-Supervised Semantic Higher-Order Smoothing (S3HOS). The S3HOS is built on a tri-partite graph based data representation of labeled and unlabeled documents that allows semantics in higher-order co-occurrence paths between terms (words)...
Information has a great value, in order to use the existing information we need to store it in a manner which can be retrieved easily when needed. So classifying the available information becomes inevitable. In addition to the existing supervised and unsupervised paradigms of classification the paper attempts to exploit the concept of semi-supervised learning paradigm. Semi-supervised learning is...
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...
This paper is to introduce a novel semi-supervised learning algorithm named linear neighborhood spread (LNS), which is capable for learning manifold structures. Labeled and unlabeled data are represented as vertices in a weighted graph, and each data point is assumed can be linearly constructed from its neighborhood. Labels are spread through the edges, and the weighted graph is regarded as probabilistic...
This paper has brought about a novel method based on multi-view algorithms for learning from positive and unlabeled examples (LPU). First we, with an improved 1-DNF method, split the text feature into a positive feature set (PF) and a negative feature set (NF). And we project each text vector on the two feature sets in turn. Then we use the co-EM SVM algorithm, which was previously used for semi-supervised...
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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