Abstract: Disclosed is a system for approximating conditional probabilities using an annotated decision tree where predictor values that did not exist in training data for the system are tracked, stored, and referenced to determine if statistical aggregation should be invoked. Further disclosed is a system for storing statistics for deriving a non-leaf probability corresponding to predictor values, and a system for aggregating such statistics to approximate conditional probabilities.

Abstract: A computer network has links for carrying data among computers, including one or more client computers. Packet loss rates are determined for the client computers. Probability distributions for the loss rates of each of the client computers are then developed using various mathematical techniques. Based on an analysis of these probability distributions, a determination is made regarding which of the links are excessively lossy.

Abstract: Clustering for purposes of data visualization and making predictions is disclosed. Embodiments of the invention are operable on a number of variables that have a predetermined representation. The variables include input-only variables, output-only variables, and both input-and-output variables. Embodiments of the invention generate a model that has a bottleneck architecture. The model includes a top layer of nodes of at least the input-only variables, one or more middle layer of hidden nodes, and a bottom layer of nodes of the output-only and the input-and-output variables. At least one cluster is determined from this model. The model can be a probabilistic neural network and/or a Bayesian network.

Abstract: A technique for reducing a model database for use with handwriting recognizers. The model database is processed with a tuning set to identify a set of models that would result in the greatest character recognition accuracy. If further model database reduction is desired, the technique iteratively identifies smaller models that have the least adverse effect on the error rate. The technique continues identifying smaller models until a desired model database size has been achieved.

Abstract: An electronic shopping aid is provided that assists a user in selecting a product from an electronic catalog of products based on their preferences for various features of the products. Since the electronic shopping aid helps a user select a product based on the user's preferences, it is referred to as a preference-based product browser. In using the browser, the user initially inputs an indication of their like or dislike for various features of the products as well as an indication of how strongly they feel about the like or dislike. The browser then utilizes this information to determine a list of products in which the user is most likely interested. As part of this determination, the browser performs collaborative filtering and bases the determination on what other users with similar characteristics (e.g., age and income) have liked.

Abstract: Visualization of high-dimensional data sets is disclosed, particularly the display of a network model for a data set. The network, such as a dependency or a Bayesian network, has a number of nodes having dependencies thereamong. The network can be displayed items and connections, corresponding to nodes and dependencies, respectively. Selection of a particular item in one embodiment results in the display of the local distribution associated with the node for the item. In one embodiment, only a predetermined number of the items are shown, such as only the items representing the most popular nodes. Furthermore, in one embodiment, in response to receiving a user input, a sub-set of the connections is displayed, proportional to the user input.

Abstract: A system that incorporates an interactive graphical user interface for visualizing clusters (categories) and segments (summarized clusters) of data. Specifically, the system automatically categorizes incoming case data into clusters, summarizes those clusters into segments, determines similarity measures for the segments, scores the selected segments through the similarity measures, and then forms and visually depicts hierarchical organizations of those selected clusters. The system also automatically and dynamically reduces, as necessary, a depth of the hierarchical organization, through elimination of unnecessary hierarchical levels and inter-nodal links, based on similarity measures of segments or segment groups. Attribute/value data that tends to meaningfully characterize each segment is also scored, rank ordered based on normalized scores, and then graphically displayed.

Abstract: One aspect of the invention is the construction of mixtures of Bayesian networks. Another aspect of the invention is the use of such mixtures of Bayesian networks to perform inferencing. A mixture of Bayesian networks (MBN) consists of plural hypothesis-specific Bayesian networks (HSBNs) having possibly hidden and observed variables. A common external hidden variable is associated with the MBN, but is not included in any of the HSBNs. The number of HSBNs in the MBN corresponds to the number of states of the common external hidden variable, and each HSBN is based upon the hypothesis that the common external hidden variable is in a corresponding one of those states. In one mode of the invention, the MBN having the highest MBN score is selected for use in performing inferencing.

Abstract: Improving object organization by presenting controlling attribute-specific lists is disclosed. For example, the object can be an email and the controlling attribute the sender of the email. Sender-specific lists are dynamically maintained and can include the most recent folders into which email have been moved. When a current email is selected, or when the user otherwise so indicates, a sender-specific list for the sender of the current email is displayed to the user. The user can select one of the folders from the list into which to move the current email. Besides email, the object can be a file, such that the controlling attribute can be the creator of the file.

Abstract: Determining the near-optimal block size for incremental-type expectation maximization (EM) algorithms is disclosed. Block size is determined based on the novel insight that the speed increase resulting from using an incremental-type EM algorithm as opposed to the standard EM algorithm is roughly the same for a given range of block sizes. Furthermore, this block size can be determined by an initial version of the EM algorithm that does not reach convergence. For a current block size, the speed increase is determined, and if the speed increase is the greatest determined so far, the current block size is set as the target block size. This process is repeated for new block sizes, until no new block sizes can be determined.

Abstract: One aspect of the invention is the construction of mixtures of Bayesian networks. Another aspect of the invention is the use of such mixtures of Bayesian networks to perform inferencing. A mixture of Bayesian networks (MBN) consists of plural hypothesis-specific Bayesian networks (HSBNs) having possibly hidden and observed variables. A common external hidden variable is associated with the MBN, but is not included in any of the HSBNs. The number of HSBNs in the MBN corresponds to the number of states of the common external hidden variable, and each HSBN is based upon the hypothesis that the common external hidden variable is in a corresponding one of those states. In one mode of the invention, the MBN having the highest MBN score is selected for use in performing inferencing.

Abstract: The invention employs mixtures of Bayesian networks to perform clustering. A mixture of Bayesian networks (MBN) consists of plural hypothesis-specific Bayesian networks (HSBNs) having possibly hidden and observed variables. A common external hidden variable is associated with the MBN, but is not included in any of the HSBNs. The number of HSBNs in the MBN corresponds to the number of states of the common external hidden variable, and each HSBN is based upon the hypothesis that the common external hidden variable is in a corresponding one of those states. In one mode of the invention, the MBN having the highest MBN score is selected for use in performing inferencing. The invention determines membership of an individual case in a cluster based upon a set of data of plural individual cases by first learning the structure and parameters of an MBN given that data and then using the MBN to compute the probability of each HSBN generating the data of the individual case.

Abstract: The invention performs speech recognition using an array of mixtures of Bayesian networks. A mixture of Bayesian networks (MBN) consists of plural hypothesis-specific Bayesian networks (HSBNs) having possibly hidden and observed variables. A common external hidden variable is associated with the MBN, but is not included in any of the HSBNs. The number of HSBNs in the MBN corresponds to the number of states of the common external hidden variable, and each HSBN models the world under the hypothesis that the common external hidden variable is in a corresponding one of those states. In accordance with the invention, the MBNs encode the probabilities of observing the sets of acoustic observations given the utterance of a respective one of said parts of speech. Each of the HSBNs encodes the probabilities of observing the sets of acoustic observations given the utterance of a respective one of the parts of speech and given a hidden common variable being in a particular state.

Abstract: An architecture for automated data analysis. In one embodiment, a computerized system comprising an automated problem formulation layer, a first learning engine, and a second learning engine. The automated problem formulation layer receives a data set. The data set has a plurality of records, where each record has a value for each of a plurality of raw transactional variables. The layer abstracts the raw transactional variables into cooked transactional variables. The first learning engine generates a model for the cooked transactional variables, while the second learning engine generates a model for the raw transactional variables.

Abstract: A method and a system for abstracting cooked variables from raw variables. In one embodiment, a data set that has a plurality of records is input into a system, where each record has a value for each of a plurality of raw transactional variables. These variables are organized into a hierarchy of nodes. The raw transactional variables are abstracted into a lesser number of cooked transactional variables, and the cooked transactional variables are output.

Abstract: A system that provides for the graphic visualization of the categories of a collection of records. The graphic visualization is referred to as “category graph.” The system optionally displays the category graph as a “similarity graph” or a “hierarchical map.” When displaying a category graph, the system displays a graphic representation of each category. The system displays the category graph as a similarity graph or a hierarchical map in a way that visually illustrates the similarity between categories. The display of a category graph allows a data analyst to better understand the similarity and dissimilarity between categories. A similarity graph includes a node for each category and an arc connecting nodes representing categories whose similarity is above a threshold. A hierarchical map is a tree structure that includes a node for each base category along with nodes representing combinations of similar categories.

Abstract: An improved belief network is provided for assisting users in making decisions. The improved belief network utilizes a decision graph in each of its nodes to store the probabilities for that node. A decision graph is a much more flexible and efficient data structure for storing probabilities than either a tree or a table, because a decision graph can reflect any equivalence relationships between the probabilities and because leaf nodes having equivalent probabilities need not be duplicated. Additionally, by being able to reflect an equivalency relationship, multiple paths (or combinations of the parent values) refer to the same probability, which yields a more accurate probability.

Abstract: A method and system for specifying a selection query for a collection of data items. The system allows a user to define a various conditions (e.g., "Supervisor=Smith") that relate to the collection. A unique icon is then assigned to represent each condition. These icon can either be assigned automatically by the system or assigned by a user. When a selection query is to be specified, the system displays a selection query grid. The selection query grid contains a row for each possible combination of the defined conditions. Each possible combination is represented by displaying the icons for the conditions in that combination in the row. A user can then select which combinations should form the selection query by selecting rows of the selection query grid. The selection query is the logical-AND of each condition or logical inverse of each condition of a selected combination and the logical-OR of all the selected combinations.

Abstract: A method and system for specifying a selection query for a collection of data items. The system allows a user to define various conditions (e.g., "Supervisor=Smith") that relate to the collection. A unique icon is then assigned to represent each condition. These icons can either be assigned automatically by the system or assigned by a user. When a selection query is to be specified, the system displays a selection query grid. The selection query grid contains a row for each possible combination of the defined conditions. Each possible combination is represented by displaying the icons for the conditions in that combination in the row. A user can then select which combinations should form the selection query by selecting rows of the selection query grid. The selection query is the logical-AND of each condition or logical inverse of each condition of a selected combination and the logical-OR of all the selected combinations.