SVM和集成学习算法的改进和实现

支持向量机(SVM)算法的主要缺点是当它处理大规模训练数据集时需要较大内存和较长的训练时间。为了加快训练速度和提高分类准确率,提出了一种融合了Bagging, SVM和Adaboost三种算法的二分类模型,并提出了一种去噪的算法。通过实验对比SVM, SVM-Adaboost以及本文提出的分类模型。随着训练数据规模不断扩大,该分类模型在提高准确率的前提下,明显提高了训练速度。

Improvement and Implementation of SVM and Integrated Learning Algorithm

The main drawback of support vector machine (SVM) algorithm is that it needs large memory and long training time while handling large training data set. In order to speed up the training and improve classification accuracy, this paper proposes a binary classification model, which fuses the Bagging, SVM and Adaboost algorithm. And a kind of denoising algorithm is proposed. Contrast SVM, the SVM-Adaboost and classification model proposed in this paper by experiment. With the expanding of training data, this classification model has improved training speed significantly under the premise of improving accuracy.