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Evolving Non-linear Stacking Ensembles for Prediction of Go Player Attributes

arXiv.org Artificial Intelligence

The paper presents an application of non-linear stacking ensembles for prediction of Go player attributes. An evolutionary algorithm is used to form a diverse ensemble of base learners, which are then aggregated by a stacking ensemble. This methodology allows for an efficient prediction of different attributes of Go players from sets of their games. These attributes can be fairly general, in this work, we used the strength and style of the players.


What is the difference between Bagging and Boosting? - Quantdare

#artificialintelligence

Bagging and Boosting are both ensemble methods in Machine Learning, but what is the key behind them? Bagging and Boosting are similar as they are both ensemble techniques, where a set of weak learners are combined to create a strong learner that obtains better performance than a single one. So, let's start from the beginning: Ensemble is a Machine Learning concept in which the idea is to train multiple models using the same learning algorithm. The ensembles take part in a bigger group of methods, called multiclassifiers where a set of hundreds or thousands of learners with a common objective are fused together to solve the problem. In the second group of multiclassifiers are the hybrid methods.


What is the difference between Bagging and Boosting?

@machinelearnbot

Bagging and Boosting are similar in that they are both ensemble techniques, where a set of weak learners are combined to create a strong learner that obtains better performance than a single one. So, let's start from the beginning: Ensemble is a Machine Learning concept in which the idea is to train multiple models using the same learning algorithm. The ensembles take part in a bigger group of methods, called multiclassifiers, where a set of hundreds or thousands of learners with a common objective are fused together to solve the problem. The second group of multiclassifiers contain the hybrid methods. They use a set of learners too, but they can be trained using different learning techniques.


Valiance Improving Predictions with Ensemble Model

#artificialintelligence

"Alone we can do so little and together we can do much" – a phrase from Helen Keller during 50's is a reflection of achievements and successful stories in real life scenarios from decades. Same thing applies to most of the cases from innovation with big impacts and with advanced technologies world. The machine Learning domain is also in the same race to make predictions and classification in a more accurate way using so called ensemble method and it is proved that ensemble modeling offers one of the most convincing way to build highly accurate predictive models. Ensemble methods are learning models that achieve performance by combining the opinions of multiple learners. Typically, an ensemble model is a supervised learning technique for combining multiple weak learners or models to produce a strong learner with the concept of Bagging and Boosting for data sampling.


Improving Predictions with Ensemble Model

#artificialintelligence

"Alone we can do so little and together we can do much" - a phrase from Helen Keller during 50's is a reflection of achievements and successful stories in real life scenarios from decades. Same thing applies with most of the cases from innovation with big impacts and with advanced technologies world. The machine Learning domain is also in the same race to make predictions and classification in a more accurate way using so called ensemble method and it is proved that ensemble modeling offers one of the most convincing way to build highly accurate predictive models. Ensemble methods are learning models that achieve performance by combining the opinions of multiple learners. Typically, an ensemble model is a supervised learning technique for combining multiple weak learners or models to produce a strong learner with the concept of Bagging and Boosting for data sampling.