Abstract: Embodiments of the subject matter can facilitate classification by leveraging proximity in space, time, and relationships. Embodiments of the subject matter can be applied to data structures including but not limited to one dimensional arrays such as an audio signals, words, and DNA sequences; two dimensional arrays such as images; three dimensional arrays such as videos or volumetric images, higher-dimensional arrays such as volumetric videos; and graphs with nodes and edges such as in chemical structures. Embodiments of the subject matter have several advantages. First, they can result in greater classification accuracy because they can leverage proximity. Second, these embodiments are more efficient and don't require as much training time because they don't require backpropagation to update parameters. Third, they can't easily be fooled by the injection of spurious information and won't easily see patterns where there are none because they don't use unsupervised learning.

Abstract: A system and method for predicting the geographic location of an Internet Protocol (IP) address are disclosed. A particular embodiment of the system and method includes receiving a model to predict a geographic coordinates position of a network device given traceroute information corresponding to an Internet Protocol (IP) address of the network device; receiving traceroute information corresponding to an Internet Protocol (IP) address of one or more beacons without requiring the geographic location of the one or more beacons associated with the traceroute information; generating, by use of a processor, an output model representing one or more predicted geographic coordinates corresponding to the network device based on the model and the traceroute information; and returning a result including the predicted geographic coordinates of the network device.

Abstract: Methods, systems, and apparatus for predicting the characteristics of a user are described. A model based on a conditional multivariate normal distribution and social relationship information between the selected user and each of one or more other users are obtained. One or more characteristics of the selected user are determined based on the model and the social relationship information. The user characteristics may be determined by adjusting the characteristics of a typical source user according to the model and the social relationship information of the selected user.

Abstract: Methods, systems, and apparatus for generating a model for predicting the characteristics of a user are described. A model template for predicting the one or more characteristics of the selected user is obtained. Training data comprising social relationship information and one or more user characteristics for each of one or more source users is obtained. One or more parameters of the model are determined based on the training data.

Abstract: A system and method for implementing a learning model for predicting the geographic location of an Internet Protocol (IP) address are disclosed. A particular embodiment of the system and method includes receiving a model to predict a geographic coordinates position of an Internet Protocol (IP) address, the model including one or more parameters and one or more variables associated with coordinates of the IP address and corresponding information associated with the IP address; receiving training data including a plurality of pairs of coordinates of a target IP address and corresponding information associated with the target IP address; determining, by use of a processor, the one or more parameters based on the training data and the model; and returning a result including information indicative of the determined parameters.

Abstract: A method and apparatus for facilitating answering a query on a database. Example embodiments include: accessing a database tree having a plurality of nodes; receiving a set of input variable values, a non-empty set of output variables, and information indicative of a node in the database tree; determining a traversal cost based on the node and the set of input variable values; determining a lower bound based on the node and the set of input variable values, wherein the lower bound corresponds to an upper-bound probability estimate based on one or more of the plurality of nodes and the set of input variable values; pruning one or more of the plurality of nodes based on the traversal cost, the lower bound, and a pruning bound; and returning a result including a non-empty set of output variable values based on the set of input variable values, the node, the traversal cost, and the lower bound.

Abstract: Methods, systems, and apparatus for generating a model for predicting the characteristics of a user are described. A model template for predicting the one or more characteristics of the selected user is obtained. Training data comprising social relationship information and one or more user characteristics for each of one or more source users is obtained. One or more parameters of the model are determined based on the training data.

Abstract: A system and method for implementing a learning model for predicting the geographic location of an Internet Protocol (IP) address are disclosed. A particular embodiment of the system and method includes receiving a model to predict a geographic coordinates position of an Internet Protocol (IP) address, the model including one or more parameters and one or more variables associated with coordinates of the IP address and corresponding information associated with the IP address; receiving training data including a plurality of pairs of coordinates of a target IP address and corresponding information associated with the target IP address; determining, by use of a processor, the one or more parameters based on the training data and the model; and returning a result including information indicative of the determined parameters.

Abstract: A method and apparatus for implementing a learning model for facilitating answering a query on a database. Example embodiments include processing logic to: receive access to a database having a plurality of rows; determine for each row in the plurality of rows which of a plurality of child nodes to associate with a row based on the row and current parameters, where the current parameters correspond to parameters of a probability distribution for the plurality of child nodes; determine new parameters for the plurality of child nodes based on associated rows of the plurality of rows; determine whether or not to return a result which indicates the new parameters based on the current parameters, the new parameters, and the plurality of rows; and return a result which indicates the new parameters.

Abstract: Methods, systems, and apparatus for predicting the characteristics of a user are described. A model based on a conditional multivariate normal distribution and social relationship information between the selected user and each of one or more other users are obtained. One or more characteristics of the selected user are determined based on the model and the social relationship information. The user characteristics may be determined by adjusting the characteristics of a typical source user according to the model and the social relationship information of the selected user.

Abstract: A system and method for implementing a learning model for predicting the geographic location of an Internet Protocol (IP) address are disclosed. A particular embodiment of the system and method includes receiving a model to predict a geographic coordinates position of an Internet Protocol (IP) address, the model including one or more parameters and one or more variables associated with coordinates of the IP address and corresponding information associated with the IP address; receiving training data including a plurality of pairs of coordinates of a target IP address and corresponding information associated with the target IP address; determining, by use of a processor, the one or more parameters based on the training data and the model; and returning a result including information indicative of the determined parameters.

Abstract: A method and apparatus for facilitating finding a nearest neighbor in a database. Example embodiments include: accessing a database tree having a plurality of nodes; receiving information indicative of a query point and information indicative of a node in the database tree; determining, by use of a processor, a lower-bound estimate based on the node and the query point, wherein the lower-bound estimate corresponds to a distance from the query point to the node; determining, by use of the processor, a temporary result corresponding to a distance to a nearest neighbor based on at least one child node of the node, the query point, and the lower-bound estimate; pruning one or more of the plurality of nodes based on the lower-bound estimate and a pruning bound; and returning a result indicative of a nearest neighbor of the query point.

Abstract: A method and apparatus for implementing a learning model for facilitating answering a query on a database. Example embodiments include processing logic to: receive access to a database having a plurality of rows; determine for each row in the plurality of rows which of a plurality of child nodes to associate with a row based on the row and current parameters, where the current parameters correspond to parameters of a probability distribution for the plurality of child nodes; determine new parameters for the plurality of child nodes based on associated rows of the plurality of rows; determine whether or not to return a result which indicates the new parameters based on the current parameters, the new parameters, and the plurality of rows; and return a result which indicates the new parameters.

Abstract: A method and apparatus for facilitating answering a query on a database. Example embodiments include: accessing a database tree having a plurality of nodes; receiving a set of input variable values, a non-empty set of output variables, and information indicative of a node in the database tree; determining a traversal cost based on the node and the set of input variable values; determining a lower bound based on the node and the set of input variable values, wherein the lower bound corresponds to an upper-bound probability estimate based on one or more of the plurality of nodes and the set of input variable values; pruning one or more of the plurality of nodes based on the traversal cost, the lower bound, and a pruning bound; and returning a result including a non-empty set of output variable values based on the set of input variable values, the node, the traversal cost, and the lower bound.

Abstract: A system and method for implementing a learning model for predicting the geographic location of an Internet Protocol (IP) address are disclosed. A particular embodiment of the system and method includes receiving a model to predict a geographic coordinates position of an Internet Protocol (IP) address, the model including one or more parameters and one or more variables associated with coordinates of the IP address and corresponding information associated with the IP address; receiving training data including a plurality of pairs of coordinates of a target IP address and corresponding information associated with the target IP address; determining, by use of a processor, the one or more parameters based on the training data and the model; and returning a result including information indicative of the determined parameters.

Abstract: A system and method for predicting the geographic location of an Internet Protocol (IP) address are disclosed. A particular embodiment of the system and method includes receiving a model to predict a geographic coordinates position of a network device given traceroute information corresponding to an Internet Protocol (IP) address of the network device; receiving traceroute information corresponding to an Internet Protocol (IP) address of one or more beacons without requiring the geographic location of the one or more beacons associated with the traceroute information; generating, by use of a processor, an output model representing one or more predicted geographic coordinates corresponding to the network device based on the model and the traceroute information; and returning a result including the predicted geographic coordinates of the network device.