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Table 2 Machine learning in CR-VANETs (summarized from [[61]])

From: Cognitive radio for vehicular ad hoc networks (CR-VANETs): approaches and challenges

Machine learning tools Application
Case based reasoning Faster convergence from exploiting previous
  solutions for example when traveling through
  same area on same day of the week
Support vector machine Signal classification and identifying
and neural networks spectrum holes, channel prediction
Reinforcement learning Channel selection, real-time learning, can
  be used in unknown environments
Genetic algorithms and Optimization of transceiver
simulated annealing parameters