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📊 Size: 253 KB
📄 Pages: 24 pages
⬇️ Downloads: 197

Summary

Multiclass learning involves finding a definition for an unknown function f(x) with a discrete range of k values (classes). Existing approaches include direct application of multiclass algorithms, learning individual binary functions for each class, and using distributed output representations. The authors propose a new technique using error-correcting codes as a distributed output representation, which improves the generalization performance of both C4.5 and backpropagation on various multiclass learning tasks. The approach is robust to changes in training sample size, assignment of representations to classes, and application of overfitting avoidance techniques. The error-correcting code technique can provide reliable class probability estimates, making it a general-purpose method for improving inductive learning program performance on multiclass problems.

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Description

Multiclass learning involves finding a definition for an unknown function f(x) with a discrete range of k values (classes).

Technical Information

  • File Format: PDF
  • File Size: 253 KB
  • Pages: 24
  • Language: EN
  • Total Downloads: 197
  • Last Updated: 4 days ago

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