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International Journal of Machine Learning and Cybernetics OnlineFirst articles

05-02-2023 | Original Article

Node embedding with capsule generation-embedding network

Achieving interpretable embedding of real network has a significant impact on network analysis tasks. However, majority of node embedding-based methods seldom consider the rationality and interpretability of node embedding. Although graph …

04-02-2023 | Original Article

A novel consensus model with probabilistic linguistic preference relation for the utilization mode selection of renewable energy sources

The exploitation and utilization of renewable energy is an important issue that contributes to the sustainable development of the national economy. This paper deals with the utilization mode selection of renewable energy in a group decision making …

04-02-2023 | Original Article

Iterative convolutional enhancing self-attention Hawkes process with time relative position encoding

Modeling Hawkes process using deep learning is superior to traditional statistical methods in the goodness of fit. However, methods based on RNN or self-attention are deficient in long-time dependence and recursive induction, respectively.

04-02-2023 | Original Article

Building hierarchical class structures for extreme multi-class learning

Class hierarchical structures play a significant role in large and complex tasks of machine learning. Existing studies on the construction of such structures follow a two-stage strategy. The category similarities are first computed with a certain …

02-02-2023 | Original Article

Bi-STAN: bilinear spatial-temporal attention network for wearable human activity recognition

With the progressive development of ubiquitous computing, wearable human activity recognition is playing an increasingly important role in many fields, such as health monitoring, disease-assisted diagnostic rehabilitation, and exercise assessment.

About this journal

Cybernetics is concerned with describing complex interactions and interrelationships between systems which are omnipresent in our daily life. Machine Learning discovers fundamental functional relationships between variables and ensembles of variables in systems. The merging of the disciplines of Machine Learning and Cybernetics is aimed at the discovery of various forms of interaction between systems through diverse mechanisms of learning from data.

The International Journal of Machine Learning and Cybernetics (IJMLC) focuses on the key research problems emerging at the junction of machine learning and cybernetics and serves as a broad forum for rapid dissemination of the latest advancements in the area. The emphasis of IJMLC is on the hybrid development of machine learning and cybernetics schemes inspired by different contributing disciplines such as engineering, mathematics, cognitive sciences, and applications. New ideas, design alternatives, implementations and case studies pertaining to all the aspects of machine learning and cybernetics fall within the scope of the IJMLC.

Key research areas to be covered by the journal include:

  • Machine Learning for modeling interactions between systems
  • Pattern Recognition technology to support discovery of system-environment interaction
  • Control of system-environment interactions
  • Biochemical interaction in biological and biologically-inspired systems
  • Learning for improvement of communication schemes between systems
Metadata
Title
International Journal of Machine Learning and Cybernetics
Coverage
Volume 1/2010 - Volume 14/2023
Publisher
Springer Berlin Heidelberg
Electronic ISSN
1868-808X
Print ISSN
1868-8071
Journal ID
13042
DOI
https://doi.org/10.1007/13042.1868-808X