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Recent Advances in Machine Learning and Computational Intelligence
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Machine learning and computational intelligence have been applied to various areas and witnessed many successes. The research in this publication explorse many intelligent algorithms which are characterized by computational adaptability, robustness, and high performance. These algorithms facilitate intelligent behavior in complex and dynamic environments and the development of technology that enables machines to think, behave, or act in a more humanesque fashion. This reprint aims to present and discuss the most recent innovations, trends, concerns, challenges, solutions, and application fields in the areas of machine learning and computational intelligence.
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Keywords
- attribute independence assumption
- attribute transformation
- Autonomous driving
- bayesian network
- beta function
- Computer science
- Computing & information technology
- conditional probability
- credit scoring
- cross-validation
- crow search algorithm
- deep learning
- differential evolution strategy
- dynamic multi-objective optimization problems
- Economics, finance, business & management
- encoder–decoder
- evolutionary algorithm
- facial skin problem
- feature importance
- generative adversarial network (GAN)
- global search optimization
- Industry & industrial studies
- Information technology industries
- jump point search algorithm
- left ventricle segmentation
- Machine learning
- Magnetic Resonance Imaging
- many-objective optimization problems
- mask R-CNN
- Media, information & communication industries
- mixed-attribute classification
- Model Predictive Control
- n/a
- naive Bayesian classifier
- optimization algorithm
- parameter adaptive
- path planning
- quadruped robot
- R5DOS intersection matrix
- reinforcement learning
- RJA-star algorithm
- search accuracy
- sentiment analysis
- subsection proximal policy optimization
- super resolution
- tactics for high performance
- thema EDItEUR::U Computing and Information Technology::UY Computer science
- TORCS
- transformer
- UNet 3+
- vectorization
- vehicle-following
- weighted importance sampling
- weighted mean of vectors
Links
DOI: 10.3390/books978-3-0365-7483-7Editions