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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Castelli, V. Cover, T.M. |
| Copyright Year | 1994 |
| Description | Author affiliation: Dept. of Electr. Eng., Stanford Univ., CA, USA (Castelli, V.; Cover, T.M.) |
| Abstract | We investigate the relative value of labeled and unlabeled samples in constructing classification rules. We observe a training set Q composed of l labeled and u unlabeled samples coming from two classes. Let sample from class 1 be distributed according to f/sub 1/(/spl middot/), samples from class 2 according to f/sub 2/(/spl middot/), and let /spl eta/ be the probability that a sample is in class 1. Assume that f/sub 1/(/spl middot/) and f/sub 2/(/spl middot/) are known and that /spl eta/ is unknown. We want to classify a new sample X/sub 0/. The relative value of labeled and unlabeled observations in reducing the probability of error is equal to I/sub t/(/spl eta/)/I/sub u/(/spl eta/), the ratio of the Fisher information of the labeled and unlabeled samples. Moreover labeled samples are not necessary in order to construct a decision rule. However, if f/sub 1/(/spl middot/) and f/sub 2/(/spl middot/) are given, but it is not known whether observations from class 1 are distributed according to f/sub 1/(/spl middot/) or according to f/sub 2/(/spl middot/), then the labeled samples are necessary and exponentially more valuable than unlabeled samples. |
| File Size | 101905 |
| File Format | |
| ISBN | 0780320158 |
| DOI | 10.1109/ISIT.1994.394877 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 1994-06-27 |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Computer aided software engineering Random variables Testing Information systems Laboratories Labeling |
| Content Type | Text |
| Resource Type | Article |
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