Question

Consider the Provided input patterns (three classes of seven letters). The first thing we need to do is to convert the patterns to input vectors. That is easy to do by assigning each "#" the value 1 and each "." the value - 1. To convert from the two-dimensional pattern to an input vector, we simply concatenate the rows, i.e., the second row of the pattern comes after the first row, the third row follows, etc.

Train a backpropagation net to classify the letters but not the fonts (one output is assigned to all the fonts of each letter).
Letter (all the fonts) Output
1 A -1,-1,-1
2 B -1,-1,+1
3 C -1,+1,-1
4 D -1,+1,+1
5 E +1,-1,-1
6 J +1,-1,+1
7 K +1,-1,-1

Answer 1

To train a backpropagation net for letter classification, input patterns of the letters must be converted into vectors with assigned values: 1 for '#' and -1 for '.'. These vectors are used to train the neural network to recognize all fonts of the same letter with a single output code, for example, 1 A -1, -1, -1.

Step-by-step explanation:

To train a backpropagation neural network to classify different fonts of the same letter, we first need to convert the provided input patterns for each font of a letter into input vectors. This conversion is achieved by assigning the value 1 to each '#', and the value -1 to each '.'. This process converts the two-dimensional patterns into one-dimensional input vectors by concatenating the rows.

For instance, converting a two-dimensional pattern that represents letter A and consists of the characters '#' and '.', would transform into a string of 1s and -1s (e.g., -1, -1, -1, etc.), based on the assigned values.

Once the patterns are converted into input vectors, a backpropagation neural network can be created and trained. With one output for each letter, the network is trained to recognize the pattern irrespective of the specific font. For example, all different fonts of the letter A will be associated with the same output, such as 1 A -1, -1, -1. The desired outputs for each letter could look like this:

A -1, -1, -1

B -1, -1, +1

C -1, +1, -1

D -1, +1, +1

E +1, -1, -1

J +1, -1, +1

K +1, -1, -1

Each font of the same letter will share the corresponding output code, ensuring that the network classifies them as the same letter, regardless of the font variations.