System and method for generating an approximation of a search engine ranking algorithm
A system and method for determining a ranking function for a search engine. A training data processor receives training data, the training data including at least a first page, a first label, a second page and a second label. A feature extraction processor receives the first page, identifies first f...
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creator | Kulkarni, Parashuram |
description | A system and method for determining a ranking function for a search engine. A training data processor receives training data, the training data including at least a first page, a first label, a second page and a second label. A feature extraction processor receives the first page, identifies first features in the first page and calculates first values relating to the first features. The feature extraction processor receives the second page and identifies second features and calculates second values relating to the second features. A machine learning processor receives the first features, the first values, the first label, the second features, the second values, and the second label. The machine learning processor generates a ranking function based on first features, the first values, the first label, the second features, the second values, and the second label. |
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A training data processor receives training data, the training data including at least a first page, a first label, a second page and a second label. A feature extraction processor receives the first page, identifies first features in the first page and calculates first values relating to the first features. The feature extraction processor receives the second page and identifies second features and calculates second values relating to the second features. A machine learning processor receives the first features, the first values, the first label, the second features, the second values, and the second label. 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A training data processor receives training data, the training data including at least a first page, a first label, a second page and a second label. A feature extraction processor receives the first page, identifies first features in the first page and calculates first values relating to the first features. The feature extraction processor receives the second page and identifies second features and calculates second values relating to the second features. A machine learning processor receives the first features, the first values, the first label, the second features, the second values, and the second label. 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A training data processor receives training data, the training data including at least a first page, a first label, a second page and a second label. A feature extraction processor receives the first page, identifies first features in the first page and calculates first values relating to the first features. The feature extraction processor receives the second page and identifies second features and calculates second values relating to the second features. A machine learning processor receives the first features, the first values, the first label, the second features, the second values, and the second label. The machine learning processor generates a ranking function based on first features, the first values, the first label, the second features, the second values, and the second label.</abstract><oa>free_for_read</oa></addata></record> |
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title | System and method for generating an approximation of a search engine ranking algorithm |
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