Learning Pinball TWSVM efficiently using Privileged Information and its Applications

Expert knowledge plays a vital role in any learning framework. However, in the field of machine learning, an expert's knowledge is rarely utilised. Furthermore, most machine learning methods (support vector machine, SVM-based) use a hinge loss function that is sensitive to noise. Thus, in order to benefit from expert knowledge while reducing noise sensitivity, we propose in this paper a fast and novel Twin Support Vector Machine classifier based on privileged information with a pinball loss function, dubbed Pin-TWSVMPI, where expert knowledge is in the form of privileged information. The proposed Pin-TWSVMPI incorporates privileged information into two nonparallel decision hyperplanes by employing a correction function. Furthermore, we employ the Sequential Minimal Optimization (SMO) technique to get the classifier in order to make computations more efficient and faster, and we have demonstrated its applicability for pedestrian detection and handwritten digit recognition. Furthermore, for UCI datasets, we first construct a process that retrieves privileged information from the dataset's features, which is then used by Pin-TWSVMPI, resulting in improved classification accuracy with