Random Machines Regression Approach: an ensemble support vector regression model with free kernel choice

03/27/2020
by   Anderson Ara, et al.
0

Machine learning techniques always aim to reduce the generalized prediction error. In order to reduce it, ensemble methods present a good approach combining several models that results in a greater forecasting capacity. The Random Machines already have been demonstrated as strong technique, i.e: high predictive power, to classification tasks, in this article we propose an procedure to use the bagged-weighted support vector model to regression problems. Simulation studies were realized over artificial datasets, and over real data benchmarks. The results exhibited a good performance of Regression Random Machines through lower generalization error without needing to choose the best kernel function during tuning process.

READ FULL TEXT
research
11/21/2019

Random Machines: A bagged-weighted support vector model with free kernel choice

Improvement of statistical learning models in order to increase efficien...
research
02/22/2023

A Generalized Weighted Loss for SVC and MLP

Usually standard algorithms employ a loss where each error is the mere a...
research
03/21/2021

Support Vector Regression Parameters Optimization using Golden Sine Algorithm and its application in stock market

Support vector machine modeling is a new approach in machine learning fo...
research
06/29/2020

GLYFE: Review and Benchmark of Personalized Glucose Predictive Models in Type-1 Diabetes

Due to the sensitive nature of diabetes-related data, preventing them fr...
research
08/29/2019

A Concert-planning Tool for Independent Musicians by Machine Learning Models

Our project aims at helping independent musicians to plan their concerts...
research
07/18/2022

pGMM Kernel Regression and Comparisons with Boosted Trees

In this work, we demonstrate the advantage of the pGMM (“powered general...
research
08/06/2018

Machine-learning error models for approximate solutions to parameterized systems of nonlinear equations

This work proposes a machine-learning framework for constructing statist...

Please sign up or login with your details

Forgot password? Click here to reset