Deep belief network (DBN) is a probabilistic generative model with multiple layers of hidden nodes and a layer of visible nodes, where parameterizations between layers obey harmonium or restricted Boltzmann machines (RBMs). In this paper we present restricted deep belief network (RDBN) for multi-view learning, where each layer of hidden nodes is composed of view-specific and shared hidden nodes...

A new methodology based on Diffusion Weighted Magnetic Resonance Imaging (DW-MRI) and Graph Theory is presented for characterizing the anatomical connections between brain gray matter areas. In a first step, brain voxels are modeled as nodes of a non-directed graph in which the weight of an arc linking two neighbor nodes is assumed to be proportional to the probability of being connected by ner...

Cutset conditioning and clique-tree propagation are two popular methods for performing exact probabilistic inference in Bayesian belief networks. Cutset conditioning is based on decomposition of a subset of network nodes, whereas clique-tree propagation depends on aggregation of nodes. We describe a means to combine cutset conditioning and clique- tree propagation in an approach called aggregat...

We discuss various aspects of secure distributed computation and look at weakening both the goals of such computation and the assumed capabilities of adversaries. We present a new protocol for a conditional form of probabilistic coordination and present a model of secure distributed computation in which friendly and hostile nodes are represented in competing interwoven networks of nodes. It is ...

Exact Bayesian inference can sometimes be performed efficiently for special cases where a function has commutative and associative symmetry of its inputs (called "causal independence"). For this reason, it is desirable to exploit such symmetry on big data sets. Here we present a method to exploit a general form of this symmetry on probabilistic adder nodes by transforming those probabilistic ad...

in this paper, a new enhanced version of the particle swarm optimization (pso) is presented. an important modification is made by adding probabilistic functions into pso, and it is named probabilistic particle swarm optimization (ppso). since the variation of the velocity of particles in pso constitutes its search engine, it should provide two phases of optimization process which are: explorati...

-Probabilistic graphs have significant importance in data mining. The correlations endure amid the adjacent edges in different probabilistic graphs. Graph clustering is used in exploratory data analysis at data compression, information retrieval and image segmentation. The existing work presented a Partially Expected Edit Distance Reduction (PEEDR) and Correlated Probabilistic Graphs Spectral (...

Probabilistic broadcast has been widely used as a flooding optimization mechanism to alleviate the effect of broadcast storm problem (BSP) in mobile ad hoc networks (MANETs). Many research studies have been carried-out to develop and evaluate the performance of this mechanism in an error-free (noiseless) environment. In reality, wireless communication channels in MANETs are an error-prone and s...

background: the axillary and supraclavicular nodal volume treatment results in improvement of local control and survival after breast conserving surgery (bcs) or modified radical mastectomy (mrm). studies on the depth of these nodes have questioned the consistent use of standard fields for all patients. this study was done to assess the dose delivery to these lymph nodes with conventional treat...

Low-energy adaptive clustering hierarchy (LEACH) protocol is studied. A cluster head optimization strategy for wireless sensor network (WSN) is proposed by using univariate marginal distribution algorithm (UMDA). The novel routing protocol is named UMDA_LEACH. It considers the overall situation in the effect of all cluster head nodes with the help of probabilistic model. By so doing, it improve...