Optical stopping rules

Efficient Stopping Rules

The importance of sampling methods in machine learning is growing due to an ever-increasing number of datasets containing millions of records of biological, medical, or other types of data. Such datasets are often beyond the reach of many standard machine learning techniques because of high computational or space complexity of the algorithms. When making even a single pass through the data is p...

When Stopping Rules Don’t Stop

Performing ranked retrieval on large document collections can be slow. The method of stopping rules has been proposed to make it more efficient. Stopping rules, which terminate search when the highest ranked documents have been determined to some degree of likelihood, are attractive and have proven useful in clustering, but have not worked well in retrieval experiments. This paper presents a st...

Stopping Rules for Turbo Decoders

Decoders for turbo codes are iterative in nature, i.e., they have to perform a certain number of iterations before reaching a satisfactory degree of confidence regarding a frame to be decoded. Until now standard turbo decoders have used a fixed number of iterations. In this article, we propose some simple “stopping rules” that can be used to reduce the average number of iterations. This techniq...

Cluster Stopping Rules For Word Sense Discrimination

As text data becomes plentiful, unsupervised methods for Word Sense Disambiguation (WSD) become more viable. A problem encountered in applying WSD methods is finding the exact number of senses an ambiguity has in a training corpus collected in an automated manner. That number is not known a priori; rather it needs to be determined based on the data itself. We address that problem using cluster ...

Optimal Stopping Rules for Directionally Reinforced Processes