2020
DOI: 10.1111/coin.12413
|View full text |Cite
|
Sign up to set email alerts
|

Learning to rank by using multivariate adaptive regression splines and conic multivariate adaptive regression splines

Abstract: 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 predicti… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1

Citation Types

0
2
0

Year Published

2022
2022
2024
2024

Publication Types

Select...
4
1

Relationship

0
5

Authors

Journals

citations
Cited by 5 publications
(2 citation statements)
references
References 38 publications
0
2
0
Order By: Relevance
“…8 The model with the minimum GCV value is considered as optimal model. 9 GCV is calculated by (3), (3) In equation 3, M is FF number, d is penalty parameter with a default value of 3, n is number of observations, ƒ(x i ) is predicted values of the MARS model.…”
Section: Andmentioning
confidence: 99%
“…8 The model with the minimum GCV value is considered as optimal model. 9 GCV is calculated by (3), (3) In equation 3, M is FF number, d is penalty parameter with a default value of 3, n is number of observations, ƒ(x i ) is predicted values of the MARS model.…”
Section: Andmentioning
confidence: 99%
“…Among the various methods based on machine learning, Multivariate Adaptive Regression Splines (MARS) is considered one of the most flexible (COOK; ZEE; RIDKER, 2004), it is parsimonious and performs better than artificial neural networks for genomic prediction in some studies (LIEW et al, 2020;LIN et al, 2008). MARS produces continuous models that can have multiple partitions, automatically models nonlinearities, and contemplates interactions of predictor variables using adaptively selected spline functions (ALTINOK; KARAGOZ; BATMAZ, 2020;TAYLAN;WEBER, 2019;.…”
Section: Introductionmentioning
confidence: 99%