Learning under concept drift: an overview
I Žliobaitė - arxiv preprint arxiv:1010.4784, 2010 - arxiv.org
Concept drift refers to a non stationary learning problem over time. The training and the
application data often mismatch in real life problems. In this report we present a context of …
application data often mismatch in real life problems. In this report we present a context of …
Machine learning-based boosted regression ensemble combined with hyperparameter tuning for optimal adaptive learning
Over the past couple of decades, many telecommunication industries have passed through
the different facets of the digital revolution by integrating artificial intelligence (AI) techniques …
the different facets of the digital revolution by integrating artificial intelligence (AI) techniques …
[BUCH][B] Computational statistics
GH Givens, JA Hoeting - 2012 - books.google.com
This new edition continues to serve as a comprehensive guide to modern and classical
methods of statistical computing. The book is comprised of four main parts spanning the …
methods of statistical computing. The book is comprised of four main parts spanning the …
[BUCH][B] Metalearning: Applications to data mining
Metalearning is the study of principled methods that exploit metaknowledge to obtain
efficient models and solutions by adapting machine learning and data mining processes …
efficient models and solutions by adapting machine learning and data mining processes …
An ensemble of cluster-based classifiers for semi-supervised classification of non-stationary data streams
Recent advances in storage and processing have provided the possibility of automatic
gathering of information, which in turn leads to fast and continuous flows of data. The data …
gathering of information, which in turn leads to fast and continuous flows of data. The data …
[BUCH][B] Graphical models: representations for learning, reasoning and data mining
Graphical models are of increasing importance in applied statistics, and in particular in data
mining. Providing a self-contained introduction and overview to learning relational …
mining. Providing a self-contained introduction and overview to learning relational …
Classification in presence of drift and latency
G Krempl, V Hofer - 2011 IEEE 11th International Conference …, 2011 - ieeexplore.ieee.org
Changes in underlying distributions over time are a challenging problem in supervised
learning. While this problem of drift is subject to an increasing effort in research, some …
learning. While this problem of drift is subject to an increasing effort in research, some …
Using a classifier pool in accuracy based tracking of recurring concepts in data stream classification
Data streams have some unique properties which make them applicable in precise
modeling of many real data mining applications. The most challenging property of data …
modeling of many real data mining applications. The most challenging property of data …
A study on adaptive learning model for performance improvement of stream analytics
JH Ku - Journal of Convergence for Information Technology, 2018 - koreascience.kr
Recently, as technologies for realizing artificial intelligence have become more common,
machine learning is widely used. Machine learning provides insight into collecting large …
machine learning is widely used. Machine learning provides insight into collecting large …
An adaptive prequential learning framework for Bayesian network classifiers
We introduce an adaptive prequential learning framework for Bayesian Network Classifiers
which attempts to handle the cost-performance trade-off and cope with concept drift. Our …
which attempts to handle the cost-performance trade-off and cope with concept drift. Our …