Solving Multiclass Learning Problems via Error-Correcting Output Codes

@article{Dietterich1995SolvingML,
  title={Solving Multiclass Learning Problems via Error-Correcting Output Codes},
  author={Thomas G. Dietterich and Ghulum Bakiri},
  journal={ArXiv},
  year={1995},
  volume={cs.AI/9501101}
}
Multiclass learning problems involve finding a definition for an unknown function f(x) whose range is a discrete set containing k > 2 values (i.e., k "classes"). The definition is acquired by studying collections of training examples of the form (xi, f(xi)). Existing approaches to multiclass learning problems include direct application of multiclass algorithms such as the decision-tree algorithms C4.5 and CART, application of binary concept learning algorithms to learn individual binary… Expand
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