Active learning SVM with regularization path for image classification

In classification problems, many different active learning techniques are often adopted to find the most informative samples for labeling in order to save human labors. Among them, active learning support vector machine (SVM) is one of the most representative approaches, in which model parameter is usually set as a fixed default value during the whole learning process. Note that model parameter is closely related to the training set. Hence dynamic parameter is desirable to make a satisfactory learning performance. To target this issue, we proposed a novel algorithm, called active learning SVM with regularization path, which can fit the entire solution path of SVM for every value of model parameters. In this algorithm, we first traced the entire solution path of the current classifier to find a series of candidate model parameters, and then used unlabeled samples to select the best model parameter. Besides, in the initial phase of training, we constructed a training sample sets by using an improved K-medoids cluster algorithm. Experimental results conducted from real-world data sets showed the effectiveness of the proposed algorithm for image classification problems.