Abstract / truncated to 115 words (read the full abstract)

We consider Bayesian networks (BNs) with discriminatively optimized parameters and structures, i.e. BNs that are optimized to maximize a kind of probabilistic margin. These maximum margin Bayesian networks (MM BNs) are inspired by support vector machines (SVMs) that aim to separate samples from different classes by a large margin in some feature space. MM BNs achieve classification performance on par with BNs optimized according to other discriminative criteria, e.g. maximum conditional likelihood. Furthermore, in several applications, they achieve classification performance comparable to that of both, linear and kernelized, SVMs. In the literature, two definitions of MM BNs with respect to their parameters are available. We analyze these definitions in terms of asymptotic consistency, extend these ... toggle 4 keywords

machine learning – bayesian networks – discriminative learning – reduced precision

Follow @eurasip