Using Parallelism to Scale Up Machine Learning to Large Data-Analysis Problems

使用并行性将机器学习扩展到大型数据分析问题

• 批准号: 9412549
• 负责人: Bruce Buchanan
• 金额: $ 24.01万
• 依托单位: University of Pittsburgh
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1995
• 资助国家: 美国
• 起止时间: 1995-04-01 至 1998-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9412549&HistoricalAwards=false
• 关键词: Using; Parallelism; Scale; Up; Machine

IRI-9412205 Etherington, David University of Oregon Eugene $62,828 - 12 mos. Toward Efficient Default Reasoning This is the first year finding of a research project involving a three year study of a novel approximation-based, approach to tractable nonmonotonic reasoning. Nonmonotonicity is a critical part of most human reasoning tasks. Despite this, current nonmonotonic reasoning formalisms are inherently undecidable in the general case, and are intractable in all but the most restrictive cases. To address this intractability, two known, weak, techniques-context-limited reasoning and fast, incomplete consistency testing-will be combined to develop a powerful, tractable, approximation mechanism. Neither of these techniques, alone, suffices, however. Since consistency is undecidable in the first-order case, context limited reasoning does not, by itself, guarantee tractability, Furthermore, known fast, incomplete consistency tests generally fail in realistically-complex knowledge bases. The goal of this research is the show that the combination of the two techniques synergistically yields a tractable approximate nonmonotonic reasoning mechanism that overcomes the limitations of either technique alone. The conditions under which this approach gives justifiable results will be formalized, and probabilistic arguments will be developed showing that the approximations induced are reasonable for certain useful types of default reasoning. Formal results will be proved showing that the approximations converge to fully-correct reasoning as additional computational resources are expended. The mechanisms necessary for building and maintaining contexts and for dealing with the errors induced by the approximation will then be studied. Finally, these ideas will be implemented and evaluated empirically against non-trivial knowledge bases.

IRI-9412205 Etherington，大卫俄勒冈州大学尤金$62，828 - 12个月。 有效的缺省推理 这是一个研究项目的第一年发现，该项目涉及一个为期三年的研究，研究一种基于近似的新方法来处理非单调推理。 非单调性是大多数人类推理任务的关键部分。 尽管如此，目前的非单调推理形式主义在一般情况下本质上是不可判定的，并且除了最严格的情况之外，在所有情况下都是难以处理的。 为了解决这个棘手的问题，两个已知的，弱，技术上下文限制推理和快速，不完整的一致性测试将结合起来，开发一个强大的，易于处理的，近似机制。 然而，单独使用这两种技术都不够。 由于一致性在一阶情况下是不可判定的，因此上下文限制推理本身并不能保证易处理性。此外，已知的快速、不完整的一致性测试通常在现实复杂的知识库中失败。 本研究的目标是表明这两种技术的结合协同产生一种易于处理的近似非单调推理机制，克服了单独使用任何一种技术的局限性。 这种方法给出合理结果的条件将被形式化，概率参数将被开发，表明诱导的近似对于某些有用类型的默认推理是合理的。 正式的结果将被证明表明，近似收敛到完全正确的推理作为额外的计算资源的消耗。 然后将研究建立和维护上下文以及处理近似引起的错误所需的机制。 最后，这些想法将实施和评估经验对非平凡的知识基础。