基于LASVM-NC和TF.RF的文本分类方法

非凸在线支持向量机（LASVM-NC）具有抗噪能力强和训练速度快的优点，而词频相关频率积（tf.rf）则是一种自适应能力很强、分类性能非常好的文本特征。通过把非凸在线支持向量机和词频相关频率积相结合，提出了一种新的文本分类方法，即LASVM-NC＋tf.rf。实验结果表明，这种方法在LASVM-NC与多种其他特征的结合中性能是最好的，且与SVM＋tf.rf相比，不仅所产生的分类器具有泛化能力更强、模型表达更稀疏的优点，而且在处理含噪声的数据时具有更好的鲁棒性，在处理大规模数据时具有快得多的训练速度。

Text classification method based on non-convex online support vector machines and term frequency relevance frequency product

Non-convex online support vector machine（LASVM-NC）has the advantages of strong anti-noise ability and fast training speed, while term frequency relevance frequency product（tf.rf）is a very good text feature for adaptive classification performance. LASVM-NC＋tf.rf is proposed as a new text classification method by combining non-convex support vector machines with term frequency relevance frequency product. It has been shown that the method can perform better than LASVM-NC plus many other features. Moreover, the method can produce faster trained and more robust classifiers with greater generalization and sparser expression than SVM＋tf.rf in processing noisy and large-scale datasets.