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Support Vector Machines (SVM), one of the new techniques for text classification, have been widely used in many application areas. SVM try to find an optimal hyperplane within the input space so as to correctly classify the binary classification problem. We present a novel heuristic text classification approach based on genetic algorithm (GA) and SVM. Simulation results demonstrate that GA and SVM...
This paper presents a k -nearest neighbor text classification algorithm based on fuzzy integral. It regards the k nearest training samples as k evidences, and fuses it using fuzzy integral, which avoids independence demand of D-S theory and improves performance of text classification. Experiment compares the new method with improved kNN algorithms and other text classification algorithms, which result...
Progressive Transductive Support Vector Machine extends Transductive Support Vector Machine in different class distribution. It is the solution to the problem that it has to estimate the ratio of positive negative examples from the sets which are not an easy task to deal with. The paper introduces a Half-Against-Half Multi-Class Text Classification algorithm Using Progressive Transductive Support...
Support vector machine(SVM ) is based on minimal structure analysis principle, it can it can solve the dimension disaster, regionally minimal problems, etc. But the common SVM can only solve binary classification. Some research develope algorithm that can solve multi-class classification through constructing binary tree with several binary SVM, the research yields some fruits. Linguistics research...
Text categorization is an important research field within text mining. A document, actually, is often full of class-independent ??general?? words which many documents and classes share. These ??general?? words do harm to text categorization rather than contribute to the task. Inspired by human cognitive procedure in text classification task, we propose a novel approach called Class Core Extraction...
In this paper, a new feature for text verification is proposed. The difficulties for the selection of features for text verification (FTV) are first discussed, followed by two principles for the FTV: the FTV should minimize the influence of backgrounds, and it should also be expressive enough for all the texts varied in structures prominently. In this paper, we exploit different block partition methods...
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...
Port state control (PSC) inspection is the most important mechanism to ensure world marine safe. Recently, some SVM-based risk assessment systems have been presented in the world. They estimate the risk of each candidate ship based on its generic factors and history inspection factors to select high-risk one before conducting on-board PSC inspection. However, how to improve the performance of the...
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