Error-correcting output codes (ECOC) have emerged in machine learning as a success- ful implementation of the idea of distributed classes. Monadic class symbols are replaced by bit strings, which are learned by an ensem- ble of binary-valued classifiers (dichotomizers). In this study, the idea of ECOC is applied to memory-based language learning with local (k- nearest neighbor) classifiers. Regression analy- sis of the experimental results reveals that, in order for ECOC to be successful for language learning, the use of the Modified Value Differ- ence Metric (MVDM) is an important factor, which is explained in terms of population den- sity of the class hyperspace.

Click Here to View the Paper In XML Format