Self-organising transparent learning system
Creators: name: Gu, Xiaowei
Publisher: name: Lancaster University
Year: 2018

description: Machine learning, as a subarea of artificial intelligence, is widely
      believed to reshape the human world in the coming decades. This thesis is
      focused on both the unsupervised and supervised self-organising
      transparent machine learning techniques. One particularly interesting
      aspect is the transparent self-organising deep learning systems.
      
      Traditional data analysis approaches and most of the machine learning
      algorithms are built upon the basis of probability theory and statistics.
      The solid mathematical foundation of the probability theory and statistics
      guarantees the good properties of these learning algorithms when the
      amount of data tends to infinity and all the data comes from the same
      distribution. However, the prior assumptions of the random nature and same
      distribution imposed on the data generation model are often too strong and
      impractical in real applications. Moreover, traditional machine learning
      algorithms also require a number of free parameters to be predefined.
      However, without any prior knowledge of the problem, which is often the
      case in real situations, the performance of the algorithms can be largely
      influenced by the improper choice. 
      
      Deep learning-based approaches are currently the state-of-the-art
      techniques in the fields of machine learning and computer vision. However,
      they are also suffering from a number of deficiencies including the
      computational burden of training using huge amount of data, lack of
      transparency and interpretation, ad hoc decisions about the internal
      structure, no proven convergence for the adaptive versions that rely on
      reinforcement learning, limited parallelisation and offline training, etc.
      These shortcomings largely all hinder the wider applications of the deep
      learning in real situations.
      
      The novel approaches presented in this thesis are developed within the
      Empirical Data Analytics framework, which is an alternative, but more
      advanced computational methodology to the traditional approaches based on
      the ensemble properties and mutual distribution of the empirical discrete
      observations. 
      
      The novel self-organising transparent machine learning algorithms
      presented in this work for clustering, regression, classification and
      anomaly detection are autonomous, self-organising, data-driven and free
      from user- and problem- specific parameters. They do not impose any data
      generation models on the data a priori, but are driven by the empirically
      observed data and are able to produce the objective results without prior
      knowledge of the problems. In addition, they are highly efficient and
      suitable for large-scale static/streaming data processing.
      
      The newly proposed self-organising transparent deep learning systems are
      able to achieve human-level performance comparable to or even better than
      the deep convolutional neural networks on image classification problems
      with the merits of being fully transparent, self-evolving, highly
      efficient, parallelisable and human-interpretable. More importantly, the
      proposed deep learning systems have the ability of starting classification
      from the very first image of each class in the same way as humans do.
      
      Numerical examples based on numerous challenging benchmark problems and
      comparisons conducted with the state-of-the-art approaches presented in
      this thesis demonstrated the validity and effectiveness of the proposed
      new machine learning algorithms and deep learning systems and show their
      potential for real applications.