Predictive learning models for concept drift
نویسندگان
چکیده
منابع مشابه
Predictive Learning Models for Concept Drift
Concept drift means that the concept about which data is obtained may shift from time to time, each time after some minimum permanence. Except for this minimum permanence, the concept shifts may not have to satisfy any further requirements and may occur infinitely often. Within this work is studied to what extent it is still possible to predict or learn values for a data sequence produced by dr...
متن کاملPredictive Learning Models for Concept
Concept drift means that the concept about which data is obtained may shift from time to time, each time after some minimum permanence. Except for this minimum permanence, the concept shifts may not have to satisfy any further requirements and may occur innnitely often. Within this work is studied to what extent it is still possible to predict or learn values for a data sequence produced by dri...
متن کاملConcept drift detection in business process logs using deep learning
Process mining provides a bridge between process modeling and analysis on the one hand and data mining on the other hand. Process mining aims at discovering, monitoring, and improving real processes by extracting knowledge from event logs. However, as most business processes change over time (e.g. the effects of new legislation, seasonal effects and etc.), traditional process mining techniques ...
متن کاملSparse Population Code Models of Word Learning in Concept Drift
Computational modeling has served a powerful tool for studying cross-situational word learning. Previous research has focused on convergence behaviors in a static environment, ignoring dynamic cognitive aspects of concept change. Here we investigate concept drift in word learning in story-telling situations. Informed by findings in cognitive neuroscience, we hypothesize that a large ensemble of...
متن کاملA Meta-learning Method for Concept Drift
The knowledge hidden in evolving data may change with time, this issue is known as concept drift. It often causes a learning system to decrease its prediction accuracy. Most existing techniques apply ensemble methods to improve learning performance on concept drift. In this paper, we propose a novel meta learning approach for this issue and develop a method: Multi-Step Learning (MSL). In our me...
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: Theoretical Computer Science
سال: 2001
ISSN: 0304-3975
DOI: 10.1016/s0304-3975(00)00274-7