Supervised Ordinal Prediction Algorithm

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A Supervised Ordinal Prediction Algorithm is a supervised prediction algorithm that can be applied by a supervised ordinal prediction system (to solve a supervised ordinal prediction task for training datasets with ordinal response variable).



References

2022

  • (Li, 2022) ⇒ Hang Li. (2022). “Learning to Rank for Information Retrieval and Natural Language Processing.” Springer Nature. ISBN:9783031021558
    • OVERVIEW: ... Many methods have been proposed for ranking creation. The methods can be categorized as the pointwise, pairwise, and listwise approaches according to the loss functions they employ. They can also be categorized according to the techniques they employ, such as the SVM based, Boosting based, and Neural Network based approaches. The author also introduces some popular learning to rank methods in details. These include: PRank, OC SVM, McRank, Ranking SVM, IR SVM, GBRank, RankNet, ListNet & ListMLE, AdaRank, SVM MAP, SoftRank, LambdaRank, LambdaMART, Borda Count, Markov Chain, and CRanking. The author explains several example applications of learning to rank including web search, collaborative filtering, definition search, keyphrase extraction, query dependent summarization, and re-ranking in machine translation. A formulation of learning for ranking creation is given in the statistical learning framework. Ongoing and future research directions for learning to rank are also discussed.

2015

2013

2009

2005

  • (Liu & Agresti, 2005) ⇒ Ivy Liu, and Alan Agresti. (2005). “The Analysis of Ordered Categorical Data: An Overview and a Survey of Recent Developments.” Test 14, no. 1
    • ABSTRACT: This article review methodologies used for analyzing ordered categorical (ordinal) response variables. We begin by surveying models for data with a single ordinal response variable. We also survey recently proposed strategies for modeling ordinal response variables when the data have some type of clustering or when repeated measurement occurs at various occasions for each subject, such as in longitudinal studies. Primary models in that case includemarginal models andcluster-specific (conditional) models for which effects apply conditionally at the cluster level. Related discussion refers to multi-level and transitional models. The main emphasis is on maximum likelihood inference, although we indicate certain models (e.g., marginal models, multi-level models) for which this can be computationally difficult. The Bayesian approach has also received considerable attention for categorical data in the past decade, and we survey recent Bayesian approaches to modeling ordinal response variables. Alternative, non-model-based, approaches are also available for certain types of inference.

1999