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International Journal of Machine Learning and Cybernetics

International Journal of Machine Learning and Cybernetics OnlineFirst articles

15-06-2019 | Original Article

Integration search strategies in tree seed algorithm for high dimensional function optimization

The tree-seed algorithm, TSA for short, is a new population-based intelligent optimization algorithm developed for solving continuous optimization problems by inspiring the relationship between trees and their seeds. The locations of trees and …

12-06-2019 | Original Article Open Access

Ensemble with estimation: seeking for optimization in class noisy data

Class noise, as know as the mislabeled data in training set, can lead to poor accuracy in classification no matter what machine learning methods are used. A reasonable estimation of class noise has a significant impact on the performance of …

07-06-2019 | Original Article

A robust multilayer extreme learning machine using kernel risk-sensitive loss criterion

More recently, extreme learning machine (ELM) has emerged as a novel computing paradigm that enables the neural network (NN) based learning to be achieved with fast training speed and good generalization performance. However, the single hidden …

07-06-2019 | Editorial

Special issue on evolutionary multi-objective optimization (EMO): theory and applications

06-06-2019 | Original Article

On selective learning in stochastic stepwise ensembles

Ensemble learning has attracted much attention of researchers studying variable selection due to its great power in improving selection accuracy and stabilizing selection results. In this paper, we present a novel ensemble pruning technique called …

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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
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