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International Journal of Machine Learning and Cybernetics

International Journal of Machine Learning and Cybernetics OnlineFirst articles

10.10.2019 | Original Article Open Access

Leveraging writing systems changes for deep learning based Chinese affective analysis

Affective analysis of social media text is in great demand. Online text written in Chinese communities often contains mixed scripts including major text written in Chinese, an ideograph-based writing system, and minor text using Latin letters, an …

10.10.2019 | Original Article

Unsupervised learning of monocular depth and ego-motion with space–temporal-centroid loss

We propose DPCNN (Depth and Pose Convolutional Network), a novel framework for monocular depth with absolute scale and camera motion estimation from videos. DPCNN uses our proposed stereo training examples, in which the spatial and temporal images …

10.10.2019 | Original Article

Granular matrix-based knowledge reductions of formal fuzzy contexts

Knowledge reduction is an important issue in formal fuzzy contexts, which can simplify the structure of concept lattices. In this paper, a novel granular matrix-based for knowledge reduction of crisp-fuzzy concept is investigated. Firstly, matrix …

09.10.2019 | Original Article

A knowledge acquisition method based on concept lattice and inclusion degree for ordered information systems

In some information system with order features, when users consider “greater than” or “less than” relations to a certain degree rather than in the full sense, using traditional methods may face great limitations. In light of natural connections …

01.10.2019 | Original Article

Ordered smooth representation clustering

The smooth representation (SMR) model is a widely used segmentation method in computer vision. This model adopts the K nearest neighbour (KNN) graph to select samples for representation. All neighbours in the KNN graph are assumed to be equally …

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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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