Abstract: A system and method for determining likelihoods of relationships between unrelated variables associated with characteristics of a user includes collecting scores for a plurality of variables and transforming the scores to discrete values. A first property having a discrete value and a second property having a discrete value are selected. How many times more likely the first property is exhibited for people who have the second property as compared to a general probability in an entire population for the first property to be exhibited is represented by computing a ratio of probabilities. The ratio of probabilities is reported.

Abstract: A system and method for discovering and displaying the compatibility between users of a search service is presented. The searching user registers with the search service and installs a web browser toolbar or component. The searching user browses web pages, and the toolbar searches each web page for identifying information. The toolbar then transmits the identifying information to a search server, which compares the identifying information with listed identifying information for other registered users. Upon finding one or more registered users corresponding to the identifying information, the search server then determines the compatibility between the searching user and each identified registered user, sending the compatibility reports back to the toolbar. The toolbar displays the compatibility reports to the searching user in the browser.

Abstract: A computer system splits a data space to partition data between processors or processes. The data space may be split into sub-regions which need not be orthogonal to the axes defined the data space's parameters, using a decision tree. The decision tree can have neural networks in each of its non-terminal nodes that are trained on, and are used to partition, training data. Each terminal, or leaf, node can have a hidden layer neural network trained on the training data that reaches the terminal node. The training of the non-terminal nodes' neural networks can be performed on one processor and the training of the leaf nodes' neural networks can be run on separate processors. Different target values can be used for the training of the networks of different non-terminal nodes. The non-terminal node networks may be hidden layer neural networks.

Abstract: A computer system splits a data space to partition data between processors or processes. The data space may be split into sub-regions which need not be orthogonal to the axes defined by the data space's parameters, using a decision tree. The decision tree can have neural networks in each of its non-terminal nodes that are trained on, and are used to partition, training data. Each terminal, or leaf, node can have a hidden layer neural network trained on the training data that reaches the terminal node. The training of the non-terminal nodes' neural networks can be performed on one processor and the training of the leaf nodes' neural networks can be run on separate processors. Different target values can be used for the training of the networks of different non-terminal nodes. The non-terminal node networks may be hidden layer neural networks.

Abstract: A database often contains sparse, i.e., under-represented, conditions which might be not represented in a training data set for training an analytical model if the training data set is created by stratified sampling. Sparse conditions may be represented in a training set by using a data set which includes essentially all of the data in a database, without stratified sampling. A series of samples, or “windows,” are used to select portions of the large data set for phases of training. In general, the first window of data should be a reasonably broad sample of the data. After the model is initially trained using a first window of data, subsequent windows are used to retrain the model. For some model types, the model is modified in order to provide it with some retention of training obtained using previous windows of data. Neural networks and Kohonen networks may be used without modification.

Abstract: The present invention provides a description of the behavior of a model that indicates the sensitivity of the model in subspaces of the input space and which indicates which dimensions of the input data are salient in subspaces of the input space. By implementing this description using a decision tree, the subspaces and their salient dimensions are both described and determined hierarchically. A sensitivity analysis is performed on the model to provide a sensitivity profile of the input space of the model according to sensitivity of outputs of the model to variations in data input to the model. The input space is divided into at least two subspaces according to the sensitivity profile. A sensitivity analysis is performed on the model to provide a sensitivity profile of each of the subspaces according to sensitivity of outputs of the model to variations in data input to the model.

Abstract: A computer system splits a data space to partition data between processors or processes. The data space may be split into sub-regions which need not be orthogonal to the axes defined by the data space's parameters, using a decision tree. The decision tree can have neural networks in each of its non-terminal nodes that are trained on, and are used to partition, training data. Each terminal, or leaf, node can have a hidden layer neural network trained on the training data that reaches the terminal node. The training of the non-terminal nodes' neural networks can be performed on one processor and the training of the leaf nodes' neural networks can be run on separate processors. Different target values can be used for the training of the networks of different non-terminal nodes. The non-terminal node networks may be hidden layer neural networks.