This book addresses the problems of modeling, prediction, classification, data understanding and processing in non-stationary and unpredictable environments. It presents major and well-known methods and approaches for the design of systems able to learn and to fully adapt its structure and to adjust its parameters according to the changes in their environments. Also presents the problem of learning in non-stationary environments, its interests, its applications and challenges and studies the complementarities and the links between the different methods and techniques of learning in evolving and non-stationary environments
In many applications of information systems learning algorithms have to act in dynamic environments ...
Dynamic processes generating time series are a phenomenon occurring in many events and systems worth...
We propose and illustrate a method for developing algorithms that can adaptively learn from data str...
Recent decades have seen rapid advances in automatization processes, supported by modern machines an...
Evolving systems unfolds from the interaction and cooperation between systems with adaptive structur...
Data stream classification is the process of learning supervised models from continuous labelled exa...
In many applications of information systems learning algorithms have to act in dynamic environments ...
Raab C. Learning in non-stationary Environments. Bielefeld: Universität Bielefeld; 2022.The topic of...
The relationship between the input and output data changes over time refer to as concept drift, whic...
Solutions present in the literature to learn in nonstationary environments can be grouped into two m...
153 p.Applications that generate data in the form of fast streams from non-stationary environments, ...
A brief introduction to machine learning for evolving data streams. In this field data is assumed in...
Nowadays most learning problems demand adaptive solutions. Current challenges include temporal data ...
Learning patterns from evolving data streams is challenging due to the characteristics of such strea...
In data stream mining, predictive models typically suffer drops in predictive performance due to con...
In many applications of information systems learning algorithms have to act in dynamic environments ...
Dynamic processes generating time series are a phenomenon occurring in many events and systems worth...
We propose and illustrate a method for developing algorithms that can adaptively learn from data str...
Recent decades have seen rapid advances in automatization processes, supported by modern machines an...
Evolving systems unfolds from the interaction and cooperation between systems with adaptive structur...
Data stream classification is the process of learning supervised models from continuous labelled exa...
In many applications of information systems learning algorithms have to act in dynamic environments ...
Raab C. Learning in non-stationary Environments. Bielefeld: Universität Bielefeld; 2022.The topic of...
The relationship between the input and output data changes over time refer to as concept drift, whic...
Solutions present in the literature to learn in nonstationary environments can be grouped into two m...
153 p.Applications that generate data in the form of fast streams from non-stationary environments, ...
A brief introduction to machine learning for evolving data streams. In this field data is assumed in...
Nowadays most learning problems demand adaptive solutions. Current challenges include temporal data ...
Learning patterns from evolving data streams is challenging due to the characteristics of such strea...
In data stream mining, predictive models typically suffer drops in predictive performance due to con...
In many applications of information systems learning algorithms have to act in dynamic environments ...
Dynamic processes generating time series are a phenomenon occurring in many events and systems worth...
We propose and illustrate a method for developing algorithms that can adaptively learn from data str...