When is it safe to use synthetic data in supervised classification? Trainable classifier technologies require large representative training sets consisting of samples labeled with their true class. Acquiring such training sets is difficult and costly. One way to alleviate this problem is to enlarge training sets by generating artificial, synthetic samples. Of course this immediately raises many questions, perhaps the first being "Why should we trust artificially generated data to be an accurate representative of the real distributions?" Other questions include "When will training on synthetic data work as well as -or better than training on real data ?".We distinguish between sample space (the set of real samples), generator space (all samples that can be generated synthetically), and finally, feature space (the set of samples in terms of finite numerical values). In this paper, we discuss a series of experiments, in which we produced synthetic data in generator space, that is, by convex interpolation among the generating parameters for samples and showed we could amplify real data to produce a classifier that is as accurate as a classifier trained on real data. Specifically, we have explored the feasibility of varying the generating parameters for Knuth's Metafont system to see if previously unseen fonts could also be recognized.
We offer a preliminary report on a research program to investigate versatile algorithms for document image content extraction, that is locating regions containing handwriting, machine-print text, graphics, line-art, logos, photographs, noise, etc. To solve this problem in its full generality requires coping with a vast diversity of document and image types. Automatically trainable methods are highly desirable, as well as extremely high speed in order to process large collections. Significant obstacles include the expense of preparing correctly labeled ("ground-truthed") samples, unresolved methodological questions in specifying the domain (e.g. what is a representative collection of document images?), and a lack of consensus among researchers on how to evaluate content-extraction performance. Our research strategy emphasizes versatility first: that is, we concentrate at the outset on designing methods that promise to work across the broadest possible range of cases. This strategy has several important implications: the classifiers must be trainable in reasonable time on vast data sets; and expensive ground-truthed data sets must be complemented by amplification using generative models. These and other design and architectural issues are discussed. We propose a trainable classification methodology that marries k-d trees and hash-driven table lookup and describe preliminary experiments.
Modern interaction systems are usually event-driven. New input devices often require new event types, and handling input from the user becomes increasingly more complex. Frequently, the WIMP (Windows, Icons, Menus, Pointer) paradigm widely used today is not suitable for interactive applications, such a virtual reality applications, that use more than the standard mouse and keyboard input devices.In this paper, we present the design and implementation of the Dynamic Event Model for Interactive System (DEMIS). DEMIS is a middleware between the operating system and the application that supports various input device events while using generic event recognition to detect composite events.
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