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The result of pre-processing an image representing the digit 0.

The result of pre-processing an image representing the digit 0.

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In this article, I'm going to apply the new, polynomial time model of artificial intelligence that I've developed to the MNIST numerical character dataset, as well as two small image datasets made with an ordinary iPhone camera. The MNIST dataset was analyzed on a supervised basis, with a success rate of 95.402%, where success is measured by the pe...

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Context 1
... The algorithm begins by reading the jpeg file into a matrix, removes any pixels that are approximately black, and then stores the locations of the remaining pixels in two column vectors that contain the horizontal and vertical index of each pixel. Figure 1 shows the result of pre-processing an image representing the digit 0. After this first step, the resulting plot of pixels is then subdivided into 121 equally sized rectangular regions, and the number of pixels in each region is then counted, and stored as an 11 × 11 matrix. The matrix is then reshaped into a 1 × 121 vector, but is otherwise unchanged. ...
Context 2
... vector serves as the input to the categorization and prediction algorithms. 4 Figure 2 shows the matrix generated for the set of pixels shown in Figure 1. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 4 5 1 0 0 0 0 0 0 0 5 7 6 9 4 0 0 0 0 0 0 6 6 0 1 8 6 2 0 0 0 0 3 8 1 0 4 9 9 0 0 0 0 0 6 8 6 8 9 9 0 0 0 0 0 0 3 6 4 3 3 0 0 This process was applied to 500 images from each of the 10 categories of digits in the MNIST training set, for a total of 5, 000 images. ...
Context 3
... The algorithm begins by reading the jpeg file into a matrix, removes any pixels that are approximately black, and then stores the locations of the remaining pixels in two column vectors that contain the horizontal and vertical index of each pixel. Figure 1 shows the result of pre-processing an image representing the digit 0. After this first step, the resulting plot of pixels is then subdivided into 121 equally sized rectangular regions, and the number of pixels in each region is then counted, and stored as an 11 × 11 matrix. The matrix is then reshaped into a 1 × 121 vector, but is otherwise unchanged. ...
Context 4
... vector serves as the input to the categorization and prediction algorithms. 4 Figure 2 shows the matrix generated for the set of pixels shown in Figure 1. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 4 5 1 0 0 0 0 0 0 0 5 7 6 9 4 0 0 0 0 0 0 6 6 0 1 8 6 2 0 0 0 0 3 8 1 0 4 9 9 0 0 0 0 0 6 8 6 8 9 9 0 0 0 0 0 0 3 6 4 3 3 0 0 This process was applied to 500 images from each of the 10 categories of digits in the MNIST training set, for a total of 5, 000 images. ...