Search engines derive revenue by displaying sponsored results along with organic results in response to user queries. In general, search engines run a per-query, on-line auction amongst interested advertisers to select sponsored results to display. In doing so, they must carefully balance the revenue derived from sponsored results against potential degradation in user experience due to less-relevant results. Hence, major search engines attempt to analyze the relevance of potential sponsored results to the user's query using supervised learning algorithms. Past work has employed a bag-of-words approach using features extracted from both the query and potential sponsored result to train the ranker.

Learning a discriminant becomes substantially more difficult when the datasets are high-dimensional and the available samples are few. This is often the case in computer vision and medical diagnosis applications. A novel Conic Section classifier (CSC) was recently introduced in the literature to handle such datasets, wherein each class was represented by a conic section parameterized by its focus, directrix and eccentricity. The discriminant boundary was the locus of all points that are equi-eccentric relative to each class-representative conic section. Simpler boundaries were preferred for the sake of generalizability.In this paper, we improve the performance of the two-class classifier via a large margin pursuit. When formulated as a non-linear optimization problem, the margin computation is demonstrated to be hard, especially due to the high dimensionality of the data. Instead, we present a geometric algorithm to compute the distance of a point to the nonlinear discriminant boundary generated by the CSC in the input space. We then introduce a large margin pursuit in the learning phase so as to enhance the generalization capacity of the classifier. We validate the algorithm on real datasets and show favorable classification rates in comparison to many existing state-of-the-art binary classifiers as well as the CSC without margin pursuit.

Many problems in computer vision involving recognition and/or classification can be posed in the general framework of supervised learning. There is however one aspect of image datasets, the high-dimensionality of the data points, that makes the direct application of off-the-shelf learning techniques problematic. In this paper, we present a novel concept class and a companion tractable algorithm for learning a suitable classifier from a given labeled dataset, that is particularly suited to high-dimensional sparse datasets. Each member class in the dataset is represented by a prototype conic section in the feature space, and new data points are classified based on a distance measure to each such representative conic section that is parameterized by its focus, directrix and eccentricity. Learning is achieved by altering the parameters of the conic section descriptor for each class, so as to better represent the data. We demonstrate the efficacy of the technique by comparing it to several well known classifiers on multiple public domain datasets.