In this paper, we examine a method for feature subset selection based on Information Theory. Initially, a framework for de ning the theoretically optimal, but computationally intractable, method for feature subset selection is presented. We show that our goal should be to eliminate a feature if it gives us little or no additional information beyond that subsumed by the remaining features. In particular, this will be the case for both irrelevant and redundant features. We then give an e cient algorithm for feature selection which computes an approximation to the optimal feature selection criterion. The conditions under which the approximate algorithm is successful are examined. Empirical results are given on a number of data sets, showing that the algorithm e ectively handles datasets with a very large number of features.

We consider acyclic directed mixed graphs, in which directed edges (x → y) and bi-directed edges (x ↔ y) may occur. A simple extension of Pearl’s d-separation criterion, called m-separation, is applied to these graphs. We introduce a local Markov property which is equivalent to the global property resulting from the m-separation criterion.

This article addresses these questions by articulating an evidential conceptual model of community synthesizing earlier definitions drawn from the literature and adding new conditions. It illustrates, by means of a small case study, how the level of community can be gauged based on evidence of core community conditions. The four conditions, purpose, commitment, context and infrastructure, we believe are necessary and sufficient for modeling and gauging intra-community "glue", and that without this glue sustainable community cannot manifest. 1 Introduction Many definitions of online community (OC) or virtual or e- or network community have been described. Broadly, publications can be divided into three areas. The first is sociological research and is well represented in the work of Barry Wellman [1,2]. Wellman contends that social network analysis, which examines community in terms of the social network of participants rather than in terms of space (neighborhoods),

The task of diagnosis is to find a hypothesis that best explains a set of manifestations (observations). Generally, it is computationally expensive to find a hypothesis because the number of the potential hypotheses is exponentially large. Recently, many efforts have been made to find parallel processing methods to solve the above difficulty. In this paper, we propose a neural network model for diagnostic problem solving where a diagnostic problem is treated as a combinatorial optimisation problem. One feature of the model is that the causal network is directly used as the network. Another feature is that the errors between the observations and the current activations of manifestation nodes are used to guide the network computing for finding optimal diagnostic hypotheses. 1 Introduction For a set of manifestations(observations), the diagnostic inference is to find the most plausible faults or disorders which can explain why the manifestations are present. In general, an individual d...

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Computation plays a major role in decision making. Even if an agent is willing to ascribe a probability to all states and a utility to all outcomes, and maximize expected utility, doing so might present serious computational problems. Moreover, computing the outcome of a given act might be difficult. In a companion paper we develop a framework for game theory with costly computation, where the objects of choice are Turing machines. Here we apply that framework to decision theory. We show how well-known phenomena like first-impression-matters biases (i.e., people tend to put more weight on evidence they hear early on), belief polarization (two people with different prior beliefs, hearing the same evidence, can end up with diametrically opposed conclusions), and the status quo bias (people are much more likely to stick with what they already have) can be easily captured in that framework. Finally, we use the framework to define some new notions: value of computational information (a computational variant of value of information) and computational value of conversation. 1

As an extension of an overview paper [Pan and McMichael, 1997] on information fusion and Causal Probabilistic Networks (CPN), this paper formalizes kernel algorithms for probabilistic inferences upon CPNs. Information fusion is realized through updating joint probabilities of the variables upon the arrival of new evidences or new hypotheses. Kernel algorithms for some dominant methods of inferences are formalized from discontiguous, mathematics-oriented literatures, with gaps lled in with regards to computability and completeness. In particular, possible optimizations on causal tree algorithm, graph triangulation and junction tree algorithm are discussed. Probanet has been designed and developed as a generic shell, or say, mother system for CPN construction and application. The design aspects and current status of Probanet are described. A few directions for research and system development are pointed out, including hierarchical structuring of network, structure decomposition and adaptive inference algorithms. This paper thus has a nature of integration including literature review, algorithm formalization and future perspective.

. Generally, the disorders in a neural network diagnosis model are assumed independent each other. In this paper, we propose a neural network model for diagnostic problem solving where the disorder independence assumption is no longer necessary. Firstly, we characterize the diagnostic tasks and the causal network which is used to represent the diagnostic problem, then we describe the neural network diagnosis model, finally, some experiment results will be given. 1 Introduction Finding explanations for a given set of events is an important aspect of general intelligent behaviour. The process of finding the best explanation was defined as Abduction by the philosopher C. S. Peirce [8]. Diagnosis is a typical abductive problem. For a set of manifestations(observations), the diagnostic inference is to find the most plausible faults or disorders which can explain the manifestations observed. In general, an individual fault or disorder can explain only a portion of the manifestations. Ther...

This paper reviews and formalizes algorithms for probabilistic inferences upon causal probabilistic networks (CPN), also known as Bayesian networks, and introduces Probanet - a development environment for CPNs. Information fusion in CPNs is realized through updating joint probabilities of the variables upon the arrival of new evidences or new hypotheses. Kernel algorithms for some dominant methods of inferences are formalized from discontiguous, mathematics-oriented literatures, with gaps filled in with regards to computability and completeness. Probanet has been designed and developed as a generic shell, a development environment for CPN construction and application. The design aspects and current status of Probanet are described. 1 Introduction Digital signal processing has entered the era of multisensor data fusion and multisource information fusion. Whatever the application may be, the process of data and information fusion generally involves multiple data types such as sensor sig...

Bayesian networks have received much attention in the recent literature. In this article, we propose an approach to learn Bayesian networks using the stochastic approximation Monte Carlo (SAMC) algorithm. Our approach has two nice features. Firstly, it possesses the self-adjusting mechanism and thus avoids essentially the local-trap problem suffered by conventional MCMC simulation-based approaches in learning Bayesian networks. Secondly, it falls into the class of dynamic importance sampling algorithms; the network features can be inferred by dynamically weighted averaging the samples generated in the learning process, and the resulting estimates can have much lower variation than the single model-based estimates. The numerical results indicate that our approach can mix much faster over the space of Bayesian networks than the conventional MCMC simulation-based approaches.