The power of sampling in knowledge discovery
Proceedings of the thirteenth ACM SIGACT-SIGMOD-SIGART symposium on …, 1994•dl.acm.org
We consider the problem of approximately verifying the truth of sentences of tuple relational
calculus in a given relation M by considering only a random sample of M. We define two
different measures for the error of a universal sentence in a relation. For a set of n universal
sentences each with at most k universal quantifiers, we give upper and lower bounds for the
sample sizes required for having a high probability that all the sentences with error at least ε
can be detected as false by considering the sample. The sample sizes are O ((log n)/ε) or O …
calculus in a given relation M by considering only a random sample of M. We define two
different measures for the error of a universal sentence in a relation. For a set of n universal
sentences each with at most k universal quantifiers, we give upper and lower bounds for the
sample sizes required for having a high probability that all the sentences with error at least ε
can be detected as false by considering the sample. The sample sizes are O ((log n)/ε) or O …
We consider the problem of approximately verifying the truth of sentences of tuple relational calculus in a given relation M by considering only a random sample of M. We define two different measures for the error of a universal sentence in a relation. For a set of n universal sentences each with at most k universal quantifiers, we give upper and lower bounds for the sample sizes required for having a high probability that all the sentences with error at least ε can be detected as false by considering the sample. The sample sizes are O((log n)/ε) or O((|M|1–1/k)log n/ε), depending on the error measure used. We also consider universal-existential sentences.
ACM Digital Library
以上显示的是最相近的搜索结果。 查看全部搜索结果