标题： 隐式约束的半监督最小二乘分类 显示英文标题

标题： Implicitly Constrained Semi-Supervised Least Squares Classification

摘要： 我们引入了一种新的半监督最小二乘分类器版本。 这种隐式约束最小二乘（ICLS）分类器在所有可能的未标记数据标记所暗示的参数集合中，最小化标记数据的平方损失。 与其他判别式半监督方法不同，我们的方法没有在目标函数中引入显式的额外假设，而是利用了监督最小二乘分类器选择中已有的隐式假设。 我们证明，从某种意义上说，我们的方法永远不会导致性能比监督分类器更差。 实验结果在基准数据集的多维情况下验证了这一理论结果，也体现在错误率方面。 显示英文摘要

摘要： We introduce a novel semi-supervised version of the least squares classifier. This implicitly constrained least squares (ICLS) classifier minimizes the squared loss on the labeled data among the set of parameters implied by all possible labelings of the unlabeled data. Unlike other discriminative semi-supervised methods, our approach does not introduce explicit additional assumptions into the objective function, but leverages implicit assumptions already present in the choice of the supervised least squares classifier. We show this approach can be formulated as a quadratic programming problem and its solution can be found using a simple gradient descent procedure. We prove that, in a certain way, our method never leads to performance worse than the supervised classifier. Experimental results corroborate this theoretical result in the multidimensional case on benchmark datasets, also in terms of the error rate.