Pattern Recognition in Collective Cognitive Systems: Hybrid
  Human-Machine Learning (HHML) By Heterogeneous Ensembles
  The ubiquitous role of the cyber-infrastructures, such as the WWW, provides
myriad opportunities for machine learning and its broad spectrum of application
domains taking advantage of digital communication. Pattern classification and
feature extraction are among the first applications of machine learning that
have received extensive attention. The most remarkable achievements have
addressed data sets of moderate-to-large size. The 'data deluge' in the last
decade or two has posed new challenges for AI researchers to design new,
effective and accurate algorithms for similar tasks using ultra-massive data
sets and complex (natural or synthetic) dynamical systems. We propose a novel
principled approach to feature extraction in hybrid architectures comprised of
humans and machines in networked communication, who collaborate to solve a
pre-assigned pattern recognition (feature extraction) task. There are two
practical considerations addressed below: (1) Human experts, such as plant
biologists or astronomers, often use their visual perception and other implicit
prior knowledge or expertise without any obvious constraints to search for the
significant features, whereas machines are limited to a pre-programmed set of
criteria to work with; (2) in a team collaboration of collective problem
solving, the human experts have diverse abilities that are complementary, and
they learn from each other to succeed in cognitively complex tasks in ways that
are still impossible imitate by machines.
