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Simultaneously, the left or right vector acting on matrix pattern in the multi-class matrixized objective function plays a role of a tradeoff parameter to balance the capacity of learning and generalization.
Particle bonding and geopolymer matrix pattern of the geopolymers were observed with scanning electron microscopy (SEM).
To our best knowledge, no direct multi-class learning method for matrix pattern has been proposed so far.
It is found that an electrode matrix pattern on each substrate, with the patterns offset, results in lower mean image error than other candidate patterns.
Inspired by the matrix-based methods used in feature extraction and selection, one matrix-pattern-oriented classification framework has been designed in our previous work and demonstrated to utilize one matrix pattern itself more effectively to improve the classification performance in practice.
It can impart only the information it was printed with, represented by a series of horizontal stripes or a matrix pattern that can be read by an optical device, like a laser.
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Matrix-pattern-oriented least squares support vector classifier (MatLSSVC) can directly classify matrix patterns and has a superior classification performance than its vector version least squares support vector classifier (LSSVC) especially for images.
Measurement tools include membrane wrinkling, traction force microscopy, elastomer micropost arrays, extracellular matrix patterning.
It is the first direct multi-class classification technique for matrix patterns.
As an extension of MatLSSVM to the SSL, LapMatLSSVM can not only directly operate on matrix patterns, but also effectively exploit the geometric information embedded in unlabeled matrix patterns.
In this paper, we, instead, explore using a set of given matrix patterns to design a classifier.
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