Add to ORKGDifferent and powerful machine learning paradigms are constantly in a race for delivering the lowest error and/or the highest comprehensibility. But what can certainly lead to better forecasting is model inter-cooperation or intra-optimization. The aim of the current talk is to put forward some recent ideas for such hybridization and optimization. Demonstrative experiments are outlined for problems coming from real, challenging environments.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech
The decision-maker is increasingly utilising machine learning (ML) techniques to find patterns in hu...
Machine Learning (ML) broadly encompasses a variety of adaptive, autonomous, and intelligent tasks w...
Making predictions nowadays is of high importance for any company, whether small or large, as thanks...
Forecasting and optimisation are two major fields of operations research that are widely used in pra...
Hybrid models, which combine multiple machine learning algorithms or optimization techniques, have s...
A time series is a series of data points indexed in time order. It can represent real world processe...
Forecast combination algorithms provide a robust solution to noisy data andshifting process dynamics...
In this paper, matters that were discussed were the algorithms used for the event prediction systems...
This paper conducts a comprehensive literature review concerning hybrid techniques that combine opt...
The interplay between optimization and machine learning is one of the most important developments in...
In this paper, matters that were discussed were the algorithms used for the event prediction systems...
Summarization: An important issue to consider when applying Machine Learning (ML) in a real world ta...
Time series forecasting has attracted the attention of the machine learning (ML) community to produc...
This book introduces numerous algorithmic hybridizations between both worlds that show how machine l...
The linear combination of forecasts is a procedure that has improved the forecasting accuracy for di...
The decision-maker is increasingly utilising machine learning (ML) techniques to find patterns in hu...
Machine Learning (ML) broadly encompasses a variety of adaptive, autonomous, and intelligent tasks w...
Making predictions nowadays is of high importance for any company, whether small or large, as thanks...
Forecasting and optimisation are two major fields of operations research that are widely used in pra...
Hybrid models, which combine multiple machine learning algorithms or optimization techniques, have s...
A time series is a series of data points indexed in time order. It can represent real world processe...
Forecast combination algorithms provide a robust solution to noisy data andshifting process dynamics...
In this paper, matters that were discussed were the algorithms used for the event prediction systems...
This paper conducts a comprehensive literature review concerning hybrid techniques that combine opt...
The interplay between optimization and machine learning is one of the most important developments in...
In this paper, matters that were discussed were the algorithms used for the event prediction systems...
Summarization: An important issue to consider when applying Machine Learning (ML) in a real world ta...
Time series forecasting has attracted the attention of the machine learning (ML) community to produc...
This book introduces numerous algorithmic hybridizations between both worlds that show how machine l...
The linear combination of forecasts is a procedure that has improved the forecasting accuracy for di...
The decision-maker is increasingly utilising machine learning (ML) techniques to find patterns in hu...
Machine Learning (ML) broadly encompasses a variety of adaptive, autonomous, and intelligent tasks w...
Making predictions nowadays is of high importance for any company, whether small or large, as thanks...