DeepAI AI Chat
Log In Sign Up

Non-convex cost functionals in boosting algorithms and methods for panel selection

02/20/2001
by   Marco Visentin, et al.
0

In this document we propose a new improvement for boosting techniques as proposed in Friedman '99 by the use of non-convex cost functional. The idea is to introduce a correlation term to better deal with forecasting of additive time series. The problem is discussed in a theoretical way to prove the existence of minimizing sequence, and in a numerical way to propose a new "ArgMin" algorithm. The model has been used to perform the touristic presence forecast for the winter season 1999/2000 in Trentino (italian Alps).

READ FULL TEXT
10/05/2015

Boosting in the presence of outliers: adaptive classification with non-convex loss functions

This paper examines the role and efficiency of the non-convex loss funct...
02/11/2020

IPBoost – Non-Convex Boosting via Integer Programming

Recently non-convex optimization approaches for solving machine learning...
10/16/2019

Dynamic Local Regret for Non-convex Online Forecasting

We consider online forecasting problems for non-convex machine learning ...
06/23/2015

GEFCOM 2014 - Probabilistic Electricity Price Forecasting

Energy price forecasting is a relevant yet hard task in the field of mul...
11/29/2018

The basins of attraction of the global minimizers of the non-convex sparse spikes estimation problem

The sparse spike estimation problem consists in estimating a number of o...
07/27/2015

A Social Spider Algorithm for Solving the Non-convex Economic Load Dispatch Problem

Economic Load Dispatch (ELD) is one of the essential components in power...