Validating Co-Training Models for Web Image Classification

Co-training is a semi-supervised learning method that is designed to take advantage of the redundancy that is present when the object to be identified has multiple descriptions. Co-training is known to work well when the multiple descriptions are conditional independent given the class of the object. The presence of multiple descriptions of objects in the form of text, images, audio and video in multimedia applications appears to provide redundancy in the form that may be suitable for co-training. In this paper, we investigate the suitability of utilizing text and image data from the Web for co-training. We perform measurements to find indications of conditional independence in the texts and images obtained from the Web. Our measurements suggest that conditional independence is likely to be present in the data. Our experiments, within a relevance feedback framework to test whether a method that exploits the conditional independence outperforms methods that do not, also indicate that better performance can indeed be obtained by designing algorithms that exploit this form of the redundancy when it is present.

LLSIM Reference Manual

A program that simulates a Digital Equipment Corporation PDP-11 computer and many of its peripherals on the AI Laboratory Time Sharing System (ITS) is described from a user's reference point of view. This simulator has a built in DDT-like command level which provides the user with the normal range of DDT facilities but also with several special debugging features built into the simulator. The DDT command language was implemented by Richard M. Stallman while the simulator was written by the author of this memo.