Two-dimensional symbol for facilitating machine learning of combined meaning of multiple ideograms contained therein
First Claim
1. A non-transitory storage medium storing instructions for execution by a processor for facilitating machine learning in a Cellular Neural Networks or Cellular Nonlinear Networks (CNN) based computing system using a stored two-dimensional symbol, the two-dimensional symbol comprising:
- a matrix of N×
N pixels of data containing a super-character, the matrix being divided into M×
M sub-matrices with each of the sub-matrices containing (N/M)×
(N/M) pixels, where N and M are positive integers, and N is a multiple of M; and
said each of the sub-matrices representing one ideogram defined in an ideogram collection set, and the super-character representing at least one meaning each formed with a specific combination of a plurality of ideograms;
wherein the instructions include learning the meaning of the super-character within the two-dimensional symbol by classifying the two-dimensional symbol via a trained convolutional neural networks model in the CNN based computing system.
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Abstract
Two-dimensional symbols with each containing multiple ideograms for facilitating machine learning are disclosed. Two-dimensional symbol comprises a matrix of N×N pixels of data representing a “super-character”. The matrix is divided into M×M sub-matrices with each of the sub-matrices containing (N/M)×(N/M) pixels. N and M are positive integers or whole numbers, and N is preferably a multiple of M. Each of the sub-matrices represents one ideogram defined in an ideogram collection set. “Super-character” represents at least one meaning each formed with a specific combination of a plurality of ideograms. Ideogram collection set includes, but is not limited to, pictograms, logosyllabic characters, Japanese characters, Korean characters, punctuation marks, numerals, special characters. Logosyllabic characters may contain one or more of Chinese characters, Japanese characters, Korean characters. Features of each ideogram can be represented by more than one layer of two-dimensional symbol.
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Citations
18 Claims
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1. A non-transitory storage medium storing instructions for execution by a processor for facilitating machine learning in a Cellular Neural Networks or Cellular Nonlinear Networks (CNN) based computing system using a stored two-dimensional symbol, the two-dimensional symbol comprising:
- a matrix of N×
N pixels of data containing a super-character, the matrix being divided into M×
M sub-matrices with each of the sub-matrices containing (N/M)×
(N/M) pixels, where N and M are positive integers, and N is a multiple of M; and
said each of the sub-matrices representing one ideogram defined in an ideogram collection set, and the super-character representing at least one meaning each formed with a specific combination of a plurality of ideograms;
wherein the instructions include learning the meaning of the super-character within the two-dimensional symbol by classifying the two-dimensional symbol via a trained convolutional neural networks model in the CNN based computing system. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18)
- a matrix of N×
Specification