1. A system for generating a classification model comprising: at least one processor; and a non-transitory computer readable medium storing instructions that when executed by the at least one processor cause the at least one processor to perform operations including: iteratively updating a classification model using training data that includes observations with known classes and coordinate vectors, the updating during selected iterations including: calculating gradients for the observations using the known classes and the classification model, calculation of a gradient for an observation in the training data comprising: calculating kernel function values using a kernel function, a coordinate vector of the observation, and coordinate vectors of other observations in the training data, and determining the gradient for the observation using the kernel function values, determining a number of observations to identify in the training data, identifying the determined number of observations in the training data using at least one of magnitudes and directions of the gradients for the observations, updating the training data by removing at least one of the identified observations, and updating the classification model based on the updated training data, and outputting the classification model.