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AssigneeMicrosoft Technology Licensing, LLC
InventorsPatrice Y. Simard, David Max Chickering, David G. Grangier, Aparna Lakshmiratan, Saleema A. Amershi
Estimating the term…
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.
A system for facilitating interactive feature selection for machine learning, the method comprising:
One or more hardware computer-readable media having embodied thereon computer-usable instructions that, when executed, facilitate a method of interactive feature selection for machine learning, the method comprising:
A method of interactive feature selection for machine learning, comprising:
Dependencies are inferred from the claim text.