Learning to Rank on Web Data by Using Multivariate Adaptive Regression Splines Technique

Date

2016-05-18

Author

Serdar, Gülşah
Karagöz, Pınar
Batmaz, İnci

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https://hdl.handle.net/11511/80677

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Learning To Rank Application On Web Data By Using Conic Multivariate Adaptive Regression Splines Technique Serdar, Gülşah; Karagöz, Pınar; Batmaz, İnci (2016-05-18)
Learning to rank by using multivariate adaptive regression splines and conic multivariate adaptive regression splines Altinok, Gulsah; Karagöz, Pınar; Batmaz, İnci (Wiley, 2020-10-22) Learning to rank is a supervised learning problem that aims to construct a ranking model for the given data. The most common application of learning to rank is to rank a set of documents against a query. In this work, we focus on point-wise learning to rank, where the model learns the ranking values. Multivariate adaptive regression splines (MARS) and conic multivariate adaptive regression splines (CMARS) are supervised learning techniques that have been proven to provide successful results on various predi...
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Citation Formats

G. Serdar, P. Karagöz, and İ. Batmaz, “Learning to Rank on Web Data by Using Multivariate Adaptive Regression Splines Technique,” 2016, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/80677.