A Parallel Detection and Prediction Method for Concept Drift in Dynamic Data Driven Application System
碩士 === 國立交通大學 === 資訊管理研究所 === 103 === The traditional data analysis and prediction method assumes that data distribution is stable. Therefore, it can predict unlabeled data precisely by analyzing the historical data. However, in today’s big-data environment, which is changing frequently, the traditi...
Main Authors: | Chiu, Yao-Ching, 邱耀慶 |
---|---|
Other Authors: | Lo, Chi-Chun |
Format: | Others |
Language: | en_US |
Published: |
2015
|
Online Access: | http://ndltd.ncl.edu.tw/handle/e864zc |
Similar Items
-
Addressing Event-Driven Concept Drift in Twitter Stream: A Stance Detection Application
by: Alessio Bechini, et al.
Published: (2021-01-01) -
Label Prediction on Dynamic Social Networks with Concept Drifting
by: Yu, Hsiang-Min, et al.
Published: (2010) -
Accurate detecting concept drift in evolving data streams
by: Myuu Myuu Wai Yan
Published: (2020-12-01) -
The concept of drift and operationalization of its detection in simulated data
by: Roded, Keren
Published: (2017) -
CDDM: Concept Drift Detection Model for Data Stream
by: Mashail Shaeel Althabiti, et al.
Published: (2020-06-01)