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"Evolutionary Machine Learning: A Survey" by Akbar Telikani, Amirhessam Tahmassebi et al.

Evolutionary Computation (EC) approaches are inspired by nature and solve optimization problems in a stochastic manner. They can offer a reliable and effective approach to address complex problems in real-world applications. EC algorithms have recently been used to improve the performance of Machine Learning (ML) models and the quality of their results. Evolutionary approaches can be used in all three parts of ML: preprocessing (e.g., feature selection and resampling), learning (e.g., parameter setting, membership functions, and neural network topology), and postprocessing (e.g., rule optimization, decision tree/support vectors pruning, and ensemble learning). This article investigates the role of EC algorithms in solving different ML challenges. We do not provide a comprehensive review of evolutionary ML approaches here; instead, we discuss how EC algorithms can contribute to ML by addressing conventional challenges of the artificial intelligence and ML communities. We look at the con ....

Machine Learning , Evolutionary Computation , Learning Optimization , Swarm Intelligence ,

Florida school orders students who get COVID vaccine to stay home 30 days

Florida school orders students who get COVID vaccine to stay home 30 days
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Multivariate Testing with Live Editor empowers brands to test personalized experiences quickly without needing engineering resources

Multivariate Testing with Live Editor empowers brands to test personalized experiences quickly without needing engineering resources
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Barilliance, a leading personalization platform for Retailers, launches full multivariate testing and optimization with a live front end visual editor. Retailers can now create personal experiences for unlimited audiences, without needing design or engineering resources.
Retailers can now create personal experiences for unlimited audiences, without needing design or engineering resources.
TEL AVIV, Israel (PRWEB)
December 16, 2020
Multiple variations can be quickly created for each experience. Key metrics are measured for each variation, empowering data driven decisions. Alternatively, brands may have machine learning capabilities automatically select winning variations and optimize traffic allocation. ....

Live Editor , Visual Editor , Test Goals , Learning Optimization , Audience Segmentation , Multivariate Testing , வாழ ஆசிரியர் , காட்சி ஆசிரியர் , சோதனை இலக்குகள் , கற்றல் தேர்வுமுறை ,