Abstract: Techniques and systems are disclosed that provide for constructing a query classification index that can be used to classify a query into relevant categories. Where documents in an index are classified into one or more category predictions for a category hierarchy, classification metadata is generated for categories to which a document in the index has been classified. Further, the classification metadata is associated to the corresponding documents in the index. Additionally, a query of the index can be classified using the metadata associated to the documents in the index, and query results can be provided that are classified by the one or more categories identified by the classification of the query.

Abstract: Interactively associating user-defined descriptions with objects. At least two users create descriptions based on provided objects. The users analyze the descriptions to determine whether the users were provided with the same or different objects. If all the users are correct in their determinations, associations between the created descriptions and the corresponding objects are adjusted. In some embodiments, the users interact via a two-player game where the input is obfuscated and the output is optionally obfuscated. For example, the users each provide a search query responsive to receipt of a search intention. If a single search intention was provided and all the users make the correct determination, the search queries are associated with the search intention.

Abstract: Providing a market design for a peer-to-peer resource exchange system. Prices for a plurality of resources such as storage space, upload bandwidth, and download bandwidth are calculated and balanced based on previous resource prices, a supply of the resources, and a demand for the resources. Further, prices for operations such as storage and retrieval are determined such that a total of the payments to resource suppliers equals a total of the payments received from the resource consumers. In some embodiments, incoming data operation requests are allocated to the peers such that equilibrium among the peers is achieved.

Abstract: Methods and systems are disclosed for learning a regression decision graph model using a Bayesian model selection approach. In a disclosed aspect, the model structure and/or model parameters can be learned using a greedy search algorithm applied to grow the model so long as the model improves. This approach enables construction of a decision graph having a model structure that includes a plurality of leaves, at least one of which includes a non-trivial linear regression. The resulting model thus can be employed for forecasting, such as for time series data, which can include single or multi-step forecasting.

Abstract: The invention provides systems and methods that can be used for targeted advertising. The system determines where to present impressions, such as advertisements, to maximize an expected utility subject to one or more constraints, which can include quotas and minimum utilities for groups of one or more impression. The traditional measure of utility in web-based advertising is click-though rates, but the present invention provides a broader definition of utility, including measures of sales, profits, or brand awareness, for example. This broader definition permits advertisements to be allocated more in accordance with the actual interests of advertisers.

Abstract: The system and method of the present invention automatically extracts the top k recommendations of objects, such as topics, items, products, books, movies, food, drinks, etc., from a local probabilistic recommendation system. Unlike prior systems, the present invention accomplishes the extraction of the top k recommendations of objects without examining a probability for every object that can be recommended. Further, the system and method of the present invention is capable of being implemented using probabilistic recommendation systems based on any conventional type of probabilistic distribution or machine learning technique, including, for example, decision trees and Bayesian networks.

Abstract: Distribution displays for categories are provided which illuminate the distribution of continuous attributes over all cases in a category, and which provide a histogram of the population of the different states of categorical attributes. An array of such displays by attribute (in one dimension) and category (in another dimension) may be provided. Category diagram displays are also provided for visualizing the different categories, and their distributions, populations, and similarities. These are displayed through different shading of nodes and edges representing categories and the relationship between two categories, and through proximity of nodes.

Abstract: Methods and systems are disclosed for learning Bayesian networks. The approach is based on specifying a search space that enables searching over equivalence classes of the Bayesian network. A set of one or more operators are applied to a representation of the equivalence class. A suitable search algorithm searches in the search space by scoring the operators locally with a decomposable scoring criteria. To facilitate application of the operators and associated scoring, validity tests can be performed to determine whether a given operator is valid relative to the current state representation.

Abstract: Continuous attributes are used as input attributes in decision tree creation. Buckets are created by dividing the range of values for the continuous attribute into sub-ranges of equal extent. These buckets form initial partitions. Mergers of adjacent partitions are considered to determine score gains from such mergers, and the most useful mergers occur. The resulting partitions are used as the discretization of the continuous attribute for use as an input attribute.

Abstract: The invention provides systems and methods that can be used for targeted advertising. The system determines where to present impressions, such as advertisements, to maximize an expected utility subject to one or more constraints, which can include quotas and minimum utilities for groups of one or more impression. The traditional measure of utility in web-based advertising is click-though rates, but the present invention provides a broader definition of utility, including measures of sales, profits, or brand awareness, for example. This broader definition permits advertisements to be allocated more in accordance with the actual interests of advertisers.

Abstract: The invention provides systems and methods that can be used for targeted advertising. The system determines where to present impressions, such as advertisements, to maximize an expected utility subject to one or more constraints, which can include quotas and minimum utilities for groups of one or more impression. The traditional measure of utility in web-based advertising is click-though rates, but the present invention provides a broader definition of utility, including measures of sales, profits, or brand awareness, for example. This broader definition permits advertisements to be allocated more in accordance with the actual interests of advertisers.

Abstract: Distribution displays for categories are provided which illuminate the distribution of continuous attributes over all cases in a category, and which provide a histogram of the population of the different states of categorical attributes. An array of such displays by attribute (in one dimension) and category (in another dimension) may be provided. Category diagram displays are also provided for visualizing the different categories, and their distributions, populations, and similarities. These are displayed through different shading of nodes and edges representing categories and the relationship between two categories, and through proximity of nodes.

Abstract: The system and method of the present invention automatically assigns “scores” to the predictor/variable value pairs of a conventional probabilistic model to measure the relative impact or influence of particular elements of a set of topics, items, products, etc. in making specific predictions using the probabilistic model. In particular, these scores measure the relative impact, either positive or negative, that the value of each individual predictor variable has on the posterior distribution of the target topic, item, product, etc., for which a probability is being determined. These scores are useful for understanding why each prediction is make, and how much impact each predictor has on the prediction. Consequently, such scores are useful for explaining why a particular prediction or recommendation was made.

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: Distribution displays for categories are provided which illuminate the distribution of continuous attributes over all cases in a category, and which provide a histogram of the population of the different states of categorical attributes. An array of such displays by attribute (in one dimension) and category (in another dimension) may be provided. Category diagram displays are also provided for visualizing the different categories, and their distributions, populations, and similarities. These are displayed through different shading of nodes and edges representing categories and the relationship between two categories, and through proximity of nodes.

Abstract: Continuous attributes are used as input attributes in decision tree creation. Buckets are created by dividing the range of values for the continuous attribute into sub-ranges of equal extent. These buckets form initial partitions. Mergers of adjacent partitions are considered to determine score gains from such mergers, and the most useful mergers occur. The resulting partitions are used as the discretization of the continuous attribute for use as an input attribute.

Abstract: Methods and systems are disclosed for learning Bayesian networks. The approach is based on specifying a search space that enables searching over equivalence classes of the Bayesian network. A set of one or more operators are applied to a representation of the equivalence class. A suitable search algorithm searches in the search space by scoring the operators locally with a decomposable scoring criteria. To facilitate application of the operators and associated scoring, validity tests can be performed to determine whether a given operator is valid relative to the current state representation.

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: Optimizing storage and retrieval of monitoring data. In one aspect of this disclosure, there is a system, method and computer readable medium that stores instructions for instructing a computer system, to optimize storage and retrieval of data. In this embodiment, a transfer manager component acquires the data from an archive and assigns predetermined storage values to specified parameters that form the data structure of the acquired data. A database stores the data acquired by the transfer manager component in accordance with the predetermined storage values. A middle tier component extracts the data in the database and interpolates the data in accordance with the predetermined storage values.

Abstract: The system and method of the present invention automatically extracts the top k recommendations of objects, such as topics, items, products, books, movies, food, drinks, etc., from a local probabilistic recommendation system. Unlike prior systems, the present invention accomplishes the extraction of the top k recommendations of objects without examining a probability for every object that can be recommended. Further, the system and method of the present invention is capable of being implemented using probabilistic recommendation systems based on any conventional type of probabilistic distribution or machine learning technique, including, for example, decision trees and Bayesian networks.