100 results about "Learning set" patented technology

Search results are processed using search requests, including analyzing received queries in order to provide a more sophisticated understanding of the information being sought. A concept network is generated from a set of queries by parsing the queries into units and defining various relationships between the units. From these concept networks, queries can be automatically categorized into categories, or more generally, can be associated with one or more nodes of a taxonomy. The categorization can be used to alter the search results or the presentation of the results to the user. As an example of alterations of search results or presentation, the presentation might include a list of “suggestions” for related search query terms. As other examples, the corpus searched might vary depending on the category or the ordering or selection of the results to present to the user might vary depending on the category. Categorization might be done using a learned set of query-node pairs where a pair maps a particular query to a particular node in the taxonomy. The learned set might be initialized from a manual indication of which queries go with which nodes and enhanced has more searches are performed. One method of enhancement involves tracking post-query click activity to identify how a category estimate of a query might have varied from an actual category for the query as evidenced by the category of the post-query click activity, e.g., a particular hits of the search results that the user selected following the query. Another method involved determining relationships between units in the form of clusters and using clustering to modify the query-node pairs.

A system for evaluating a view, including a first mapper for encoding an image of a view according to a first parameter to create a first map having multiple defined areas, a second mapper for encoding the image according to a second parameter to create a second map having multiple defined areas, an overlap mapper for combining the maps to create an overlap map, a tabulator for measuring areas in the overlap map corresponding to overlapping defined areas, creating a set of measurements of the image, and an analyzer for analyzing the set of measurements of the image and a learning set of measurement groups with associated values to compute an estimated value associated with the image where the estimated value relates to the set of measurements of the image in the same manner that each value in the learning set relates to its associated measurement group.

In a pre-processing step prior to training a learning machine, pre-processing includes reducing the quantity of features to be processed using feature selection methods selected from the group consisting of recursive feature elimination (RFE), minimizing the number of non-zero parameters of the system (l0-norm minimization), evaluation of cost function to identify a subset of features that are compatible with constraints imposed by the learning set, unbalanced correlation score, transductive feature selection and single feature using margin-based ranking. The features remaining after feature selection are then used to train a learning machine for purposes of pattern classification, regression, clustering and / or novelty detection.

A method of configuring settings in an imaging device includes retrieving the previous state of a plurality of settings in the imaging device. The new state of the settings in the imaging device are determined. The new state is combined with the previous state to form an optimal state of the settings. The imaging device is then configured with the settings according to the optimal state.

A method of configuring settings in an imaging device includes configuring the settings with default values. If at least one value for the settings is externally adjusted, the settings in the imaging device are configured with the at least one value. If the user indicates that the new settings are acceptable, the imaging device determines whether to store the new settings for use in calculating new default values.

The invention discloses an automatic intra-vehicle environment adjustment method and device. The automatic intra-vehicle environment adjustment method includes the steps of obtaining the current scene mode and pre-learnt setting habits of intra-vehicle environment parameters by a driver when monitoring that the driver drives a vehicle, and automatically adjusting relevant intra-vehicle environment parameters according to the current scene mode and the pre-learnt setting habits of the intra-vehicle environment parameters by the driver. According to the automatic intra-vehicle environment adjustment method and device, intra-vehicle environment learning can be conducted according to the habits of the driver, and the intra-vehicle environment parameters are automatically set or adjusted according to different scenes when the driver drives the vehicle, so that the driver drives the vehicle more comfortably and safely.