Learning To Rank Application On Web Data By Using Conic Multivariate Adaptive Regression Splines Technique

2016-05-18

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Learning to Rank on Web Data by Using Multivariate Adaptive Regression Splines Technique
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Learning to rank web data using multivariate adaptive regression splines
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A new trend, called learning to rank, has recently come to light in a wide variety of applications in Information Retrieval (IR), Natural Language Processing (NLP), and Data Mining (DM), to utilize machine learning techniques to automatically build the ranking models. Typical applications are document retrieval, expert search, definition search, collaborative filtering, question answering, and machine translation. In IR, there are three approaches used for ranking. The one is traditional model approaches su...
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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 Application On Web Data By Using Conic Multivariate Adaptive Regression Splines Technique,” 2016, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/74323.