## Abstract

We study a learning problem which allows for a `fair' comparison between unsupervised learning methods - probabilistic model construction, and more traditional algorithms that directly learn a classification. The merits of each approach are intuitively clear: inducing a model is more expensive computationally, but may support a wider range of predictions. Its performance, however, will depend on how well the postulated probabilistic model fits that data. To compare the paradigms we consider a model which postulates a single binary-valued hidden variable on which all other attributes depend. In this model, finding the most likely value of any one variable (given known values for the others) reduces to testing a linear function of the observed values. We learn the model with two techniques: the standard EM algorithm, and a new algorithm we develop based on covariances. We compare these, in a controlled fashion, against an algorithm (a version of Winnow) that attempts to find a good linear classifier directly. Our conclusions help delimit the fragility of using a model that is even `slightly' simpler than the distribution actually generating the data, vs. the relative robustness of directly searching for a good predictor.

Original language | English (US) |
---|---|

Pages (from-to) | 123-141 |

Number of pages | 19 |

Journal | Machine Learning |

Volume | 42 |

Issue number | 1-2 |

DOIs | |

State | Published - 2001 |

## ASJC Scopus subject areas

- Software
- Artificial Intelligence