Abstract: A recharging system for portable electronic devices that have specialized recharging ports and accept only specifically shaped connectors. The recharging system includes a plug connector for engaging an external power source. A generic terminal connector is also provided. A wire cord is used to connect the plug connector and the terminal connector. At least one adaptor is provided. Each adaptor has a receptacle and a specialized connector. The receptacle selectively receives and electrically interconnects with the generic terminal connector. The specialized connector on the adaptor selectively engages the portable electronic device, therein electrically interconnecting the portable electronic device to the generic terminal connector. The portable electronic device is then recharged by the flow of electricity through the generic terminal connector.

Abstract: A recharging system for portable electronic devices that have specialized recharging ports and accept only specifically shaped connectors. The recharging system includes a plug connector for engaging an external power source. A generic terminal connector is also provided. A wire cord is used to connect the plug connector and the terminal connector. At least one adaptor is provided. Each adaptor has a receptacle and a specialized connector. The receptacle selectively receives and electrically interconnects with the generic terminal connector. The specialized connector on the adaptor selectively engages the portable electronic device, therein electrically interconnecting the portable electronic device to the generic terminal connector. The portable electronic device is then recharged by the flow of electricity through the generic terminal connector.

Abstract: An improved belief network generator is provided. A belief network is generated utilizing expert knowledge retrieved from an expert in a given field of expertise and empirical data reflecting observations made in the given field of the expert. In addition to utilizing expert knowledge and empirical data, the belief network generator provides for the use of continuous variables in the generated belief network and missing data in the empirical data.

Abstract: Method and apparatus for efficiently performing nearest neighbor queries on a database of records wherein each record has a large number of attributes by automatically extracting a multidimensional index from the data. The method is based on first obtaining a statistical model of the content of the data in the form of a probability density function. This density is then used to decide how data should be reorganized on disk for efficient nearest neighbor queries. At query time, the model decides the order in which data should be scanned. It also provides the means for evaluating the probability of correctness of the answer found so far in the partial scan of data determined by the model. In this invention a clustering process is performed on the database to produce multiple data clusters. Each cluster is characterized by a cluster model. The set of clusters represent a probability density function in the form of a mixture model.

Abstract: An improved belief network generator is provided. A belief network is generated utilizing expert knowledge retrieved from an expert in a given field of expertise and empirical data reflecting observations made in the given field of the expert. In addition to utilizing expert knowledge and empirical data, the belief network generator provides for the use of continuous variables in the generated belief network and missing data in the empirical data.

Abstract: An improved belief network generator is provided. In a preferred embodiment of the present invention, a belief network is generated utilizing expert knowledge retrieved from an expert in a given field of expertise and empirical data reflecting observations made in the given field of the expert. In addition to utilizing expert knowledge and empirical data, the belief network generator of the preferred embodiment provides for the use of continuous variables in the generated belief network and missing data in the empirical data.

Abstract: An improved belief network generator is provided. A belief network is generated utilizing expert knowledge retrieved from an expert in a given field of expertise and empirical data reflecting observations made in the given field of the expert. In addition to utilizing expert knowledge and empirical data, the belief network generator of the preferred embodiment provides for the use of continuous variables in the generated belief network and missing data in the empirical data.