Stratified Sampling Large Relational Networks Using Topologically Divided Stratums
نویسندگان
چکیده
منابع مشابه
A New Relational Networks Sampling Algorithm Using Topologically Divided Stratums
One popular solution to deal with large-scale relational networks is to derive a representative sample from huge relational networks. We expect this sample could represent the origin relational network well so that the sampled network can be used for simulations and further analysis instead of the origin one. In this paper, we propose a network stratified sampling algorithm using topologically ...
متن کاملTopologically ordered competitive sampling
-An tmportant problem in nonparametrtc modehng ts the selecHon of samples from an mdependent and Mentwally dtstrtbuted observatton (liD) process such that the resultmg sample ensemble forms a prototyptcal model of the observaHon process. Thts paper discusses a sampling techmque that is closely related to topologtcally ordered compettttve learning In the context of a ttghtly constrained notton o...
متن کاملOptimizing Speech Recognition Evaluation Using Stratified Sampling
Producing large enough quantities of high-quality transcriptions for accurate and reliable evaluation of an automatic speech recognition (ASR) system can be costly. It is therefore desirable to minimize the manual transcription work for producing metrics with an agreed precision. In this paper we demonstrate how to improve ASR evaluation precision using stratified sampling. We show that by alte...
متن کاملCancer Prognosis Prediction Using Balanced Stratified Sampling
High accuracy in cancer prediction is important to improve the quality of the treatment and to improve the rate of survivability of patients. As the data volume is increasing rapidly in the healthcare research, the analytical challenge exists in double. The use of effective sampling technique in classification algorithms always yields good prediction accuracy. The SEER public use cancer databas...
متن کاملImportance Sampling on Relational Bayesian Networks
We present techniques for importance sampling from distributions defined by Relational Bayesian Networks. The methods operate directly on the abstract representation language, and therefore can be applied in situations where sampling from a standard Bayesian Network representation is infeasible. We describe experimental results from using standard, adaptive and backward sampling strategies. Fur...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Procedia Engineering
سال: 2011
ISSN: 1877-7058
DOI: 10.1016/j.proeng.2011.08.707