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

Date

2016-05-18

Author

Serdar, Gülşah
Karagöz, Pınar
Batmaz, İnci

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https://hdl.handle.net/11511/74323

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Learning to Rank on Web Data by Using Multivariate Adaptive Regression Splines Technique Serdar, Gülşah; Karagöz, Pınar; Batmaz, İnci (null; 2016-05-18)
Learning to rank web data using multivariate adaptive regression splines Altınok, Gülşah; Batmaz, İnci; Karagöz, Pınar; Department of Statistics (2018) 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...
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...
Learning to Generate Unambiguous Spatial Referring Expressions for Real-World Environments Dagan, Fethiye Irmak; Kalkan, Sinan; Leite, Iolanda (2019-01-01) Referring to objects in a natural and unambiguous manner is crucial for effective human-robot interaction. Previous research on learning-based referring expressions has focused primarily on comprehension tasks, while generating referring expressions is still mostly limited to rule-based methods. In this work, we propose a two-stage approach that relies on deep learning for estimating spatial relations to describe an object naturally and unambiguously with a referring expression. We compare our method to the...
Learning to play an imperfect information card game using reinforcement learning Alpaslan, Ferda Nur; Baykal, Ömer; Demirdöver, Buğra Kaan (2022-08-01)

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.