Statistical learning theory studies the process of inferring regularities from empirical data. The fundamental problem is what is called generalization: how it is possible to infer a law which will be valid for an infinite number of future observations, given only a finite amount of data? This problem hinges upon fundamental issues of statistics and science in general, such as the problems of complexity of explanations, a priori knowledge, and representation of data.
We provide our clients with classifiers that are optimized to their specific data and protect against over fitting. Support Vector machines, generalized discriminants and logistic regression are among our more popular techniques.