  Artificial neural networks use a lot of coefficients that take a great deal
of computing power for their adjustment, especially if deep learning networks
are employed. However, there exist coefficients-free extremely fast
indexing-based technologies that work, for instance, in Google search engines,
in genome sequencing, etc. The paper discusses the use of indexing-based
methods for pattern recognition. It is shown that for pattern recognition
applications such indexing methods replace with inverse patterns the fully
inverted files, which are typically employed in search engines. Not only such
inversion provide automatic feature extraction, which is a distinguishing mark
of deep learning, but, unlike deep learning, pattern inversion supports almost
instantaneous learning, which is a consequence of absence of coefficients. The
paper discusses a pattern inversion formalism that makes use on a novel pattern
transform and its application for unsupervised instant learning. Examples
demonstrate a view-angle independent recognition of three-dimensional objects,
such as cars, against arbitrary background, prediction of remaining useful life
of aircraft engines, and other applications. In conclusion, it is noted that,
in neurophysiology, the function of the neocortical mini-column has been widely
debated since 1957. This paper hypothesize that, mathematically, the cortical
mini-column can be described as an inverse pattern, which physically serves as
a connection multiplier expanding associations of inputs with relevant pattern
classes.
