Abstract: A collection of data that is extremely large can be difficult to search and/or analyze. Relevance may be dramatically improved by automatically classifying queries and web pages in useful categories, and using these classification scores as relevance features. A thorough approach may require building a large number of classifiers, corresponding to the various types of information, activities, and products. Creation of classifiers and schematizers is provided on large data sets. Exercising the classifiers and schematizers on hundreds of millions of items may expose value that is inherent to the data by adding usable meta-data. Some aspects include active labeling exploration, automatic regularization and cold start, scaling with the number of items and the number of classifiers, active featuring, and segmentation and schematization.

Abstract: Innovations for category-prefixed data batching (“CPDB”) of entropy-coded data or other payload data for coded media data, as well as innovations for corresponding recovery of the entropy-coded data (or other payload data) formatted with CPDB. The CPDB can be used in conjunction with coding/decoding for video content, image content, audio content or another type of content. For example, after receiving coded media data in multiple categories from encoding units, a formatting tool formats payload data with CPDB, generating a batch prefix for a batch of the CPDB-formatted payload data. The batch prefix includes a category identifier and a data quantity indicator. The formatting tool outputs the CPDB-formatted payload data to a bitstream. At the decoder side, a formatting tool receives the CPDB-formatted payload data in a bitstream, recovers the payload data from the CPDB-formatted payload data, and outputs the payload data (e.g., to decoding units).

Abstract: A collection of data that is extremely large can be difficult to search and/or analyze. Relevance may be dramatically improved by automatically classifying queries and web pages in useful categories, and using these classification scores as relevance features. A thorough approach may require building a large number of classifiers, corresponding to the various types of information, activities, and products. Creation of classifiers and schematizers is provided on large data sets. Exercising the classifiers and schematizers on hundreds of millions of items may expose value that is inherent to the data by adding usable meta-data. Some aspects include active labeling exploration, automatic regularization and cold start, scaling with the number of items and the number of classifiers, active featuring, and segmentation and schematization.

Abstract: A collection of data that is extremely large can be difficult to search and/or analyze. Relevance may be dramatically improved by automatically classifying queries and web pages in useful categories, and using these classification scores as relevance features. A thorough approach may require building a large number of classifiers, corresponding to the various types of information, activities, and products. Creation of classifiers and schematizers is provided on large data sets. Exercising the classifiers and schematizers on hundreds of millions of items may expose value that is inherent to the data by adding usable meta-data. Some aspects include active labeling exploration, automatic regularization and cold start, scaling with the number of items and the number of classifiers, active featuring, and segmentation and schematization.

Abstract: The claimed subject matter includes techniques for providing an application testing service. An example method includes receiving context information from a client system, the context information comprising parameters that describe details of a user interaction with an application under test (AUT). The method also includes receiving a set of potential actions from the client system. The method also includes identifying a selected action from the set of potential actions and sending the selected action to the client system, wherein the AUT is customized based on the selected action. The method also includes receiving reward data from the client system based on a user's interaction with the AUT. The method also includes storing the context information, the selected action, and the reward data to a log of application test data.

Abstract: Innovations for category-prefixed data batching (“CPDB”) of entropy-coded data or other payload data for coded media data, as well as innovations for corresponding recovery of the entropy-coded data (or other payload data) formatted with CPDB. The CPDB can be used in conjunction with coding/decoding for video content, image content, audio content or another type of content. For example, after receiving coded media data in multiple categories from encoding units, a formatting tool formats payload data with CPDB, generating a batch prefix for a batch of the CPDB-formatted payload data. The batch prefix includes a category identifier and a data quantity indicator. The formatting tool outputs the CPDB-formatted payload data to a bitstream. At the decoder side, a formatting tool receives the CPDB-formatted payload data in a bitstream, recovers the payload data from the CPDB-formatted payload data, and outputs the payload data (e.g., to decoding units).

Abstract: Innovations for category-prefixed data batching (“CPDB”) of entropy-coded data or other payload data for coded media data, as well as innovations for corresponding recovery of the entropy-coded data (or other payload data) formatted with CPDB. The CPDB can be used in conjunction with coding/decoding for video content, image content, audio content or another type of content. For example, after receiving coded media data in multiple categories from encoding units, a formatting tool formats payload data with CPDB, generating a batch prefix for a batch of the CPDB-formatted payload data. The batch prefix includes a category identifier and a data quantity indicator. The formatting tool outputs the CPDB-formatted payload data to a bitstream. At the decoder side, a formatting tool receives the CPDB-formatted payload data in a bitstream, recovers the payload data from the CPDB-formatted payload data, and outputs the payload data (e.g., to decoding units).

Abstract: A method, system, and machine-readable medium for classifying an image element as one of a plurality of categories, including assigning the image element based on a ratio between an unoccluded perimeter of the image element and an occluded perimeter of the image element and coding the image element according to a coding scheme associated with the category to which the image element is classified. Exemplary applications include image compression, where categories include image foreground and background layers.

Abstract: A collection of data that is extremely large can be difficult to search and/or analyze. Relevance may be dramatically improved by automatically classifying queries and web pages in useful categories, and using these classification scores as relevance features. A thorough approach may require building a large number of classifiers, corresponding to the various types of information, activities, and products. Creation of classifiers and schematizers is provided on large data sets. Exercising the classifiers and schematizers on hundreds of millions of items may expose value that is inherent to the data by adding usable meta-data. Some aspects include active labeling exploration, automatic regularization and cold start, scaling with the number of items and the number of classifiers, active featuring, and segmentation and schematization.

Abstract: A method, system, and machine-readable medium for classifying an image element as one of a plurality of categories, including assigning the image element based on a ratio between an unoccluded perimeter of the image element and an occluded perimeter of the image element and coding the image element according to a coding scheme associated with the category to which the image element is classified. Exemplary applications include image compression, where categories include image foreground and background layers.

Abstract: A collection of data that is extremely large can be difficult to search and/or analyze. Relevance may be dramatically improved by automatically classifying queries and web pages in useful categories, and using these classification scores as relevance features. A thorough approach may require building a large number of classifiers, corresponding to the various types of information, activities, and products. Creation of classifiers and schematizers is provided on large data sets. Exercising the classifiers and schematizers on hundreds of millions of items may expose value that is inherent to the data by adding usable meta-data. Some aspects include active labeling exploration, automatic regularization and cold start, scaling with the number of items and the number of classifiers, active featuring, and segmentation and schematization.

Abstract: A method, system, and machine-readable medium for classifying an image element as one of a plurality of categories, including assigning the image element based on a ratio between an unoccluded perimeter of the image element and an occluded perimeter of the image element and coding the image element according to a coding scheme associated with the category to which the image element is classified. Exemplary applications include image compression, where categories include image foreground and background layers.

Abstract: A system, method and computer-readable media are introduced that relate to data coding and decoding. A computing device encodes received data such as video data into a base layer of compressed video and an enhancement layer of compressed video. The computing device controls drift introduced into the base layer of the compressed video. The computing device, such as a scalable video coder, allows drift bay predicting the base layer from the enhancement layer information. The amount of drift is managed to improve overall compression efficiency.

Abstract: A collection of data that is extremely large can be difficult to search and/or analyze. Relevance may be dramatically improved by automatically classifying queries and web pages in useful categories, and using these classification scores as relevance features. A thorough approach may require building a large number of classifiers, corresponding to the various types of information, activities, and products. Creation of classifiers and schematizers is provided on large data sets. Exercising the classifiers and schematizers on hundreds of millions of items may expose value that is inherent to the data by adding usable meta-data. Some aspects include active labeling exploration, automatic regularization and cold start, scaling with the number of items and the number of classifiers, active featuring, and segmentation and schematization.

Abstract: A collection of data that is extremely large can be difficult to search and/or analyze. Relevance may be dramatically improved by automatically classifying queries and web pages in useful categories, and using these classification scores as relevance features. A thorough approach may require building a large number of classifiers, corresponding to the various types of information, activities, and products. Creation of classifiers and schematizers is provided on large data sets. Exercising the classifiers and schematizers on hundreds of millions of items may expose value that is inherent to the data by adding usable meta-data. Some aspects include active labeling exploration, automatic regularization and cold start, scaling with the number of items and the number of classifiers, active featuring, and segmentation and schematization.

Abstract: A system, method and computer-readable media are introduced that relate to data coding and decoding. A computing device encodes received data such as video data into a base layer of compressed video and an enhancement layer of compressed video. The computing device controls drift introduced into the base layer of the compressed video. The computing device, such as a scalable video coder, allows drift bay predicting the base layer from the enhancement layer information. The amount of drift is managed to improve overall compression efficiency.

Abstract: A collection of data that is extremely large can be difficult to search and/or analyze. Relevance may be dramatically improved by automatically classifying queries and web pages in useful categories, and using these classification scores as relevance features. A thorough approach may require building a large number of classifiers, corresponding to the various types of information, activities, and products. Creation of classifiers and schematizers is provided on large data sets. Exercising the classifiers and schematizers on hundreds of millions of items may expose value that is inherent to the data by adding usable meta-data. Some aspects include active labeling exploration, automatic regularization and cold start, scaling with the number of items and the number of classifiers, active featuring, and segmentation and schematization.

Abstract: A method, system, and machine-readable medium for classifying an image element as one of a plurality of categories, including assigning the image element based on a ratio between an unoccluded perimeter of the image element and an occluded perimeter of the image element and coding the image element according to a coding scheme associated with the category to which the image element is classified. Exemplary applications include image compression, where categories include image foreground and background layers.

Abstract: The claimed subject matter includes techniques for providing an application testing service. An example method includes receiving context information from a client system, the context information comprising parameters that describe details of a user interaction with an application under test (AUT). The method also includes receiving a set of potential actions from the client system. The method also includes identifying a selected action from the set of potential actions and sending the selected action to the client system, wherein the AUT is customized based on the selected action. The method also includes receiving reward data from the client system based on a user's interaction with the AUT. The method also includes storing the context information, the selected action, and the reward data to a log of application test data.

Abstract: A system, method and computer-readable media are introduced that relate to data coding and decoding. A computing device encodes received data such as video data into a base layer of compressed video and an enhancement layer of compressed video. The computing device controls drift introduced into the base layer of the compressed video. The computing device, such as a scalable video coder, allows drift by predicting the base layer from the enhancement layer information. The amount of drift is managed to improve overall compression efficiency.