NDLI: Multiobjective model selection for non-linear regression techniques

Content Provider	IEEE Xplore Digital Library
Author	Pasolli, L. Notarnicola, C. Bruzzone, L.
Copyright Year	2010
Description	Author affiliation: Institute for Applied Remote Sensing, EURAC Research, Viale Druso, 1 I-39100 Bolzano Italy (Notarnicola, C.) \|\| Dep. of Information Engineering and Computer Science, University of Trento, Via Sommarive, 14, I-38123 Italy (Pasolli, L.; Bruzzone, L.)
Abstract	This paper proposes to model the critical issue of the choice of the free parameters of a supervised non-linear regression technique (the so called model selection issue) as a multiobjective optimization problem. In this framework, the multi-objective function is made up of a set of two or more quality metrics (e.g., MSE, $R^{2},$ etc.) computed on the test (or validation) samples. A set of solutions is derived according to the concept of Pareto optimality. The advantages of the proposed approach with respect to the traditional ones (which typically optimize a single scalar metric) are mainly two: 1) the capability to derive solutions which jointly optimize the set of metrics considered and represent different possible optimal tradeoffs among them; and 2) the possibility for the user to effectively select the model that optimizes the requirements of the specific retrieval problem. Results achieved for the specific application of soil moisture estimation from microwave remotely sensed data with the Support Vector Regression (SVR) technique are reported. These results show the effectiveness of the proposed approach.
Starting Page	268
Ending Page	271
File Size	204040
Page Count	4
File Format	PDF
ISBN	9781424495658
ISSN	21537003
e-ISBN	9781424495665
e-ISBN	9781424495641
DOI	10.1109/IGARSS.2010.5649190
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-07-25
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Optimization Estimation Remote sensing Soil moisture Dielectric measurements Measurement uncertainty Support Vector Regression (SVR) Regression Model Selection Multi-Objective Optimization Biophysical Parameters
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Multi-Objective Parameter Optimization in Support Vector Regression: General Formulation and Application to the Retrieval of Soil Moisture From Remote Sensing Data

Estimating Soil Moisture With the Support Vector Regression Technique

Crop backscatter modeling and soil moisture estimation with support vector regression

Active Learning Methods for Biophysical Parameter Estimation

Multioutput Support Vector Regression for Remote Sensing Biophysical Parameter Estimation

Multi-frequency Portable Dielectric Probes For In-situ Soil Moisture Measurement

Application of multi-output support vector regression in remore sensing inversions

A novel hybrid approach to the estimation of biophysical parameters from remotely sensed data

Data Processing Technique for Deriving Soil Water Spectroscopic Parameters in Microwave

Multiobjective model selection for non-linear regression techniques

Similar Documents

Multi-Objective Parameter Optimization in Support Vector Regression: General Formulation and Application to the Retrieval of Soil Moisture From Remote Sensing Data

Estimating Soil Moisture With the Support Vector Regression Technique

Crop backscatter modeling and soil moisture estimation with support vector regression

Active Learning Methods for Biophysical Parameter Estimation

Multioutput Support Vector Regression for Remote Sensing Biophysical Parameter Estimation

Multi-frequency Portable Dielectric Probes For In-situ Soil Moisture Measurement

Application of multi-output support vector regression in remore sensing inversions

A novel hybrid approach to the estimation of biophysical parameters from remotely sensed data

Data Processing Technique for Deriving Soil Water Spectroscopic Parameters in Microwave

Multiobjective model selection for non-linear regression techniques