A method for analysis and classification of flow cytometry data, the method comprising: downloading an input dataset comprising a plurality of assays performed on a plurality of samples by flow cytometry into a computer system comprising a processor and a storage device, wherein the processor is programmed to execute at least one support vector machine and performs the steps of: pre-processing a first portion of the input dataset with one or more feature selection algorithm to select a subset of assays from the plurality of assays; training a support vector machine to separate the first portion of the input dataset using the selected subset of assays, testing the trained support vector machine with a second portion of the input dataset using the selected subset of assays to determine whether an optimal solution is achieved, and, until an optimal solution is reached, repeating the steps of training and testing the support vector machine; once an optimal solution is reached, inputting a live data set of flow cytometry data into the computer system; processing the live data set using the selected subset of assays using the trained and tested support vector machine to produce a result comprising a classification of the flow cytometry data into one of two distinct classes; and generating an output display at a display device with an identification of a flow cytometry data classification; the method characterized in that the support vector machine utilizes a distributional kernel to separate the selected subset of assays into two probability distributions and measure a divergence between the two probability distributions. downloading an input dataset comprising a plurality of assays performed on a plurality of samples by flow cytometry into a computer system comprising a processor and a storage device, wherein the processor is programmed to execute at least one support vector machine and performs the steps of: pre-processing a first portion of the input dataset with one or more feature selection algorithm to select a subset of assays from the plurality of assays; training a support vector machine to separate the first portion of the input dataset using the selected subset of assays, testing the trained support vector machine with a second portion of the input dataset using the selected subset of assays to determine whether an optimal solution is achieved, and, until an optimal solution is reached, repeating the steps of training and testing the support vector machine; once an optimal solution is reached, inputting a live data set of flow cytometry data into the computer system; processing the live data set using the selected subset of assays using the trained and tested support vector machine to produce a result comprising a classification of the flow cytometry data into one of two distinct classes; and generating an output display at a display device with an identification of a flow cytometry data classification; pre-processing a first portion of the input dataset with one or more feature selection algorithm to select a subset of assays from the plurality of assays; training a support vector machine to separate the first portion of the input dataset using the selected subset of assays, testing the trained support vector machine with a second portion of the input dataset using the selected subset of assays to determine whether an optimal solution is achieved, and, until an optimal solution is reached, repeating the steps of training and testing the support vector machine; once an optimal solution is reached, inputting a live data set of flow cytometry data into the computer system; processing the live data set using the selected subset of assays using the trained and tested support vector machine to produce a result comprising a classification of the flow cytometry data into one of two distinct classes; and generating an output display at a display device with an identification of a flow cytometry data classification; the method characterized in that the support vector machine utilizes a distributional kernel to separate the selected subset of assays into two probability distributions and measure a divergence between the two probability distributions.