How to Shift Bias: Lessons from the Baldwin Effect

@article{Turney1996HowTS,
  title={How to Shift Bias: Lessons from the Baldwin Effect},
  author={Peter D. Turney},
  journal={Evolutionary Computation},
  year={1996},
  volume={4},
  pages={271-295}
}
An inductive learning algorithm takes a set of data as input and generates a hypothesis as output. A set of data is typically consistent with an infinite number of hypotheses; therefore, there must be factors other than the data that determine the output of the learning algorithm. In machine learning, these other factors are called the bias of the learner. Classical learning algorithms have a fixed bias, implicit in their design. Recently developed learning algorithms dynamically adjust their… CONTINUE READING

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