Recently a model for supervised learning of probabilistic transduc ers represented by su x trees was introduced However this algo rithm tends to build very large trees requiring very large amounts of computer memory In this paper we propose a new more com pact transducer model in which one shares the parameters of distri butions associated to contexts yielding similar conditional output distrib...

Previous algorithms for the construction of Bayesian belief network structures from data have been either highly dependent on con­ ditional independence (CI) tests, or have re­ quired an ordering on the nodes to be sup­ plied by the user. We present an algorithm that integrates these two approaches CI tests are used to generate an ordering on the nodes from the database which is then used to re...

in this paper, we introduce and study a mixed variational inclusion problem involving infinite family of fuzzy mappings. an iterative algorithm is constructed for solving a mixed variational inclusion problem involving infinite family of fuzzy mappings and the convergence of iterative sequences generated by the proposed algorithm is proved. some illustrative examples are also given.

We present a new type of Markov Chain algo rithm for the calculation of combined Dempster Shafer belief which is almost linear in the size of the frame thus making the calculation of belief feasible for a wider range of problems We also indicate how these algorithms may be used in the cal culation of belief in product spaces associated with networks

Due to the great importa uling problem with open ticular surgeon. The su considered to be close t algorithm is from dyna of states. The objective o use efficiency and minim especially for large size rithm of Fei et al. does

In this paper an algorithm for the calculation of the root locus of fractional linear systems is presented. The proposed algo rithm takes advantage of present day computational resources and processes directly the characteristic equation, avoiding the limitations revealed by standard methods. The results demonstrate the good performance for different types of expressions.

Irrelevance-based partial MAPs are useful constructs for domain-independent explana­ tion using belief networks. We look at two definitions for such partial MAPs, and prove important properties that are useful in de­ signing algorithms for computing them effec­ tively. We make use of these properties in modifying our standard MAP best-first algo­ rithm, so as to handle irrelevance-based par­ ti...

We give geometrical conditions, which guarantee the existence of a diffeomorphism in order to transform a nonlinear system without inputs into a canonical normal form depending on its output. Moreover we extend our results to a class of systems with inputs. We end this paper by some examples and its simulations to highlight the proposed algo rithm.

sive com plexity. A reason for th is , we th in k , m ay be due to th e underly ing approach itself. In th is p ap er we in troduce a novel, sem iotic based m odel of language w hich provides us w ith a sim ple a lgo rithm for language pro­

We present two algorithms for exact and ap­ proximate inference in causal networks. The first algorithm, dynamic conditioning, is a re­ finement of cutset conditioning that has lin­ ear complexity on some networks for which cutset conditioning is exponential. The sec­ ond algorithm, B-conditioning, is an algo­ rithm for approximate inference that allows one to trade-off the quality of approxima...