论文标题
K-Means内核分类器
K-Means Kernel Classifier
论文作者
论文摘要
我们将K-均值聚类与最小二乘内核分类方法相结合。 K-均值聚类用于为每个类提取一组代表向量。最小二乘内核方法使用这些代表性向量作为分类任务的训练集。我们表明，无监督和监督的学习算法的这种组合表现良好，我们使用MNIST数据集说明了这种方法
We combine K-means clustering with the least-squares kernel classification method. K-means clustering is used to extract a set of representative vectors for each class. The least-squares kernel method uses these representative vectors as a training set for the classification task. We show that this combination of unsupervised and supervised learning algorithms performs very well, and we illustrate this approach using the MNIST dataset
