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



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...
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 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)
Artificial intelligence and machine learning are widely popular in many areas. One of the most popular ones is gaming. Games are perfect testbeds for machine learning and artificial intelligence with various scenarios and types. This study aims to develop a self-learning intelligent agent to play the Hearts game. Hearts is one of the most popular trick-taking card games around the world. It is an imperfect information card game. In addition to having a huge state space, Hearts offers many extra challenges d...
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.