Hard and soft classifiers are two important groups of techniques for classification problems. Logistic regression and Support Vector Machines are typical examples of soft and hard classifiers respectively. The essential difference between these two groups is whether one needs to estimate the class conditional probability for the classification task or not. In particular, soft classifiers predic...

We consider perceptron-like algorithms with margin in which the standard classification condition is modified to require a specific value of the margin in the augmented space. The new algorithms are shown to converge in a finite number of steps and used to approximately locate the optimal weight vector in the augmented space following a procedure analogous to Bolzano’s bisection method. We demo...

In classification, semisupervised learning involves a large amount of unlabeled data with only a small number of labeled data. This imposes great challenge in that the class probability given input can not be well estimated through labeled data alone. To enhance predictability of classification, this article introduces a large margin semisupervised learning method constructing an efficient loss...

This paper studies the binary classification problem associated with a family of Lipschitz convex loss functions called large-margin unified machines (LUMs), which offers natural bridge between distribution-based likelihood approaches and margin-based approaches. LUMs can overcome so-called data piling issue support vector machine in high-dimension low-sample size setting, while their theoretic...