The statistical pattern recognition based on Bayes formula implies the concept of mutually exclusive classes. This assumption is not applicable when we have to identify some non-exclusive properties and therefore it is unnatural in biological neural networks. Considering the framework of probabilistic neural networks we propose statistical identification of non-exclusive properties by using one...

Mobile Data Offloading permits users to use cheap communication media, whenever feasible, for delivering their personal data instead of using the infrastructure which is more expensive. Having good procedures to assess and compare the different possibilities a device has to send his data is crucial. In the following paper, we propose an evaluating approach that takes into consideration the topo...

A two-factor experiment with interaction between factors wherein observations follow an Inverse Gaussian model is considered. Analysis of the experiment is approached via an empirical Bayes procedure. The conjugate family of prior distributions is considered. Bayes and empirical Bayes estimators are derived. Application of the procedure is illustrated on a data set, which has previously been an...

We describe a novel approach to tackle intention recognition, by combining dynamically configurable and situation-sensitive Causal Bayes Networks plus plan generation techniques. Given some situation, such networks enable the recognizing agent to come up with the most likely intentions of the intending agent, i.e. solve one main issue of intention recognition. And, in case of having to make a q...

One of the fundamental issues of Bayesian networks is their representational power, re-ecting what kind of functions they can or cannot represent. In this paper, we rst prove an upper bound on the representational power of Augmented Naive Bayes. We then extend the result to general Bayesian networks. Roughly speaking, if a function contains an m-XOR, there exists no Bayesian networks with node ...

We present a generalization bound for feedforward neural networks in terms of the product of the spectral norms of the layers and the Frobenius norm of the weights. The generalization bound is derived using a PAC-Bayes analysis.

We review recent results about the maximal values of the Kullback-Leibler information divergence from statistical models defined by neural networks, including näıve Bayes models, restricted Boltzmann machines, deep belief networks, and various classes of exponential families. We illustrate approaches to compute the maximal divergence from a given model starting from simple subor super-models. W...