Lagrangean-Based Combinatorial Optimization for Large-Scale S³VMs

Abstract

The process of manually labeling instances, essential to a supervised classifier, can be expensive and time-consuming. In such a scenario the semisupervised approach, which makes the use of unlabeled patterns when building the decision function, is a more appealing choice. Indeed, large amounts of unlabeled samples often can be easily obtained. Many optimization techniques have been developed in the last decade to include the unlabeled patterns in the support vector machines formulation. Two broad strategies are followed: continuous and combinatorial. The approach presented in this paper belongs to the latter family and is especially suitable when a fair estimation of the proportion of positive and negative samples is available. Our method is very simple and requires a very light parameter selection. Several medium- and large-scale experiments on both artificial and real-world data sets have been carried out proving the effectiveness and the efficiency of the proposed algorithm.

A fast and effective detection framework for whole-slide histopathology image analysis

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A novel density ratio‐based batch active learning fault diagnosis method integrated with adaptive Laplacian graph trimming

“…As is known, samples from the lowdensity region are more valuable for constructing the classifier, since the ideal classification hyperplane often passes through the sparse data region with the lowest density in the feature space. [17] Local reachable density (LRD) [18,19] and local outlier factor (LOF) [20,21] can effectively evaluate the density of samples. LRD represents the density of samples based on the K-distance neighbourhood, [22] and LOF represents the average ratios of the LRD of the sample neighbourhood points to the LRD of the sample.…”