scholarly journals ERM and RERM are optimal estimators for regression problems when malicious outliers corrupt the labels

2020 ◽  
Vol 14 (2) ◽  
pp. 3563-3605
Author(s):  
Geoffrey Chinot
Keyword(s):  
Regression Problems ◽  
Optimal Estimators

An Optimization Technique for Solving a Class of Ridge Fuzzy Regression Problems

2021 ◽  
Author(s):  
Delara Karbasi ◽  
Alireza Nazemi ◽  
Mohammad Reza Rabiei
Keyword(s):  
Optimization Technique ◽  
Fuzzy Regression ◽  
Regression Problems

K-Means clustering based Extreme Learning ANFIS with improved interpretability for regression problems

2021 ◽  
Vol 215 ◽  
pp. 106750
Author(s):  
C.P. Pramod ◽  
G.N. Pillai
Keyword(s):  
Regression Problems

scholarly journals Projection neural network for a class of sparse regression problems with cardinality penalty

2021 ◽  
Vol 431 ◽  
pp. 188-200
Author(s):  
Wenjing Li ◽  
Wei Bian
Keyword(s):  
Neural Network ◽  
Sparse Regression ◽  
Regression Problems ◽  
Projection Neural Network

A Bayesian Approach to Optimum Allocation in Regression Problems

1970 ◽  
Vol 19 (1) ◽  
pp. 45-52 ◽  
Author(s):  
Bimal Kumar Sinha
Keyword(s):  
Bayesian Approach ◽  
Optimum Allocation ◽  
Regression Problems

scholarly journals A NEW LINEAR GENETIC PROGRAMMING APPROACH BASED ON STRAIGHT LINE PROGRAMS: SOME THEORETICAL AND EXPERIMENTAL ASPECTS

2009 ◽  
Vol 18 (05) ◽  
pp. 757-781 ◽  
Author(s):  
CÉSAR L. ALONSO ◽  
JOSÉ LUIS MONTAÑA ◽  
JORGE PUENTE ◽  
CRUZ ENRIQUE BORGES
Keyword(s):  
Data Structure ◽  
Genetic Programming ◽  
Computer Programs ◽  
Symbolic Regression ◽  
Programming Approach ◽  
Linear Genetic Programming ◽  
Straight Line ◽  
Structured Representations ◽  
Regression Problems ◽  
Straight Line Programs

Tree encodings of programs are well known for their representative power and are used very often in Genetic Programming. In this paper we experiment with a new data structure, named straight line program (slp), to represent computer programs. The main features of this structure are described, new recombination operators for GP related to slp's are introduced and a study of the Vapnik-Chervonenkis dimension of families of slp's is done. Experiments have been performed on symbolic regression problems. Results are encouraging and suggest that the GP approach based on slp's consistently outperforms conventional GP based on tree structured representations.

scholarly journals Boosted Mixture of Experts: An Ensemble Learning Scheme

1999 ◽  
Vol 11 (2) ◽  
pp. 483-497 ◽  
Author(s):  
Ran Avnimelech ◽  
Nathan Intrator
Keyword(s):  
Recognition Task ◽  
Mixture Of Experts ◽  
Data Set ◽  
Combination Model ◽  
Digit Recognition ◽  
Learning Procedure ◽  
Nist Database ◽  
Boosting Algorithm ◽  
Regression Problems ◽  
Classification And Regression

We present a new supervised learning procedure for ensemble machines, in which outputs of predictors, trained on different distributions, are combined by a dynamic classifier combination model. This procedure may be viewed as either a version of mixture of experts (Jacobs, Jordan, Nowlan, & Hinton, 1991), applied to classification, or a variant of the boosting algorithm (Schapire, 1990). As a variant of the mixture of experts, it can be made appropriate for general classification and regression problems by initializing the partition of the data set to different experts in a boostlike manner. If viewed as a variant of the boosting algorithm, its main gain is the use of a dynamic combination model for the outputs of the networks. Results are demonstrated on a synthetic example and a digit recognition task from the NIST database and compared with classical ensemble approaches.

scholarly journals Hierarchical Matching and Regression with Application to Photometric Redshift Estimation

2016 ◽  
Vol 12 (S325) ◽  
pp. 145-155
Author(s):  
Fionn Murtagh
Keyword(s):  
Information Structure ◽  
Data Analytics ◽  
A Priori ◽  
Sloan Digital Sky Survey ◽  
Photometric Redshifts ◽  
Merging Galaxies ◽  
Photometric Redshift ◽  
Sky Survey ◽  
Wide Range ◽  
Regression Problems

AbstractThis work emphasizes that heterogeneity, diversity, discontinuity, and discreteness in data is to be exploited in classification and regression problems. A global a priori model may not be desirable. For data analytics in cosmology, this is motivated by the variety of cosmological objects such as elliptical, spiral, active, and merging galaxies at a wide range of redshifts. Our aim is matching and similarity-based analytics that takes account of discrete relationships in the data. The information structure of the data is represented by a hierarchy or tree where the branch structure, rather than just the proximity, is important. The representation is related to p-adic number theory. The clustering or binning of the data values, related to the precision of the measurements, has a central role in this methodology. If used for regression, our approach is a method of cluster-wise regression, generalizing nearest neighbour regression. Both to exemplify this analytics approach, and to demonstrate computational benefits, we address the well-known photometric redshift or ‘photo-z’ problem, seeking to match Sloan Digital Sky Survey (SDSS) spectroscopic and photometric redshifts.

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