# ECOC

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## Abstract

### Approaches to multiclass learning problems

- direct application of multiclass algorithms, such as C4.5 and CART
- application of binary concept learning algorithms to learn individual binary functions for each of the $k$ classes
- application of binary concept learning algorithms with distributed output representations.

### New techinique

Error-correcting codes are employed as a distributed output representation

It is robust with respect to

- changes in the size of the training sample
- assignment of distributed representations to particular pruning 3, application of overfitting avoidance techniques such as decision-tree pruning.

And it can provide reliable class probability estimates.

## Introduction

### Two class

- decision-tree methods, such as C4.5 and CART.
- artificial neural network algorithms, such as the perceptron algorithm and the error BP algorithm.

### Multiclass

- direct multiclass approach: generalize decision-tree algorithms
- one-per-class approach: learn one binary function for each class with connectionist algorithms. ($f_i,i=1,2,\ldots,k$)
- distributed output code.