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Error-Correcting Output Codes: A General Method for Improving Multiclass Inductive Learning Programs
| Content Provider | Scilit |
|---|---|
| Author | Dietterich, Thomas G. |
| Copyright Year | 2018 |
| Description | 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 large collections of training examples of the form 〈x$ _{ i }$,f(x$ _{ i }$)〉. Existing approaches to this problem include (a) direct application of multiclass algorithms such as the decision-tree algorithms ID3 and CART, (b) application of binary concept learning algorithms to learn individual binary functions for each of the k classes, and (c) application of binary concept learning algorithms with distributed output codes such as those employed by Sejnowski and Rosenberg in the NETtalk system.$ ^{20}$ This chapter compares these three approaches to a new technique in which BCH error-correcting codes are employed as a distributed output representation. We show that these output representations improve the performance of ID3 on the NETtalk task and of backpropagation on an isolated-letter speech-recognition task. These results demonstrate that error-correcting output codes provide a general-purpose method for improving the performance of inductive learning programs on multiclass problems. Book Name: The Mathematics of Generalization |
| Related Links | https://content.taylorfrancis.com/books/download?dac=C2017-0-77031-2&isbn=9780429492525&doi=10.1201/9780429492525-14&format=pdf |
| Ending Page | 407 |
| Page Count | 13 |
| Starting Page | 395 |
| DOI | 10.1201/9780429492525-14 |
| Language | English |
| Publisher | Informa UK Limited |
| Publisher Date | 2018-10-14 |
| Access Restriction | Open |
| Subject Keyword | Book Name: The Mathematics of Generalization Functions Binary Multiclass Error Correcting Output Codes |
| Content Type | Text |
| Resource Type | Chapter |
