MNIST handwritten digit classification

MNSIT simply stands for Modified National Institute of Standards and Technology dataset. The application is based on machine learning of the huge data set available, and it helps to recognize a particular digit into a class of 10. The application is widely used in visual training and digit recognition. The application also uses many algorithms and classifiers.

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Concepts used:

The dataset of the project has to be checked first, as they vary from 0 to 255. The method of changing the IDX into a simpler CSV is also used. There are many classifiers used for the project, such as KNN (K nearest neighbors). The KNN categorizes the data set into the different values of K. We have to consider the parameter of K based on the dataset provided.

Project Implementation

The first step should be calculating the distance between the test data point and all the labeled data points. Then there should be arranging of the data points of increasing order of the distance between them. Distance functions can be used for this case, and there are many functions that can be helpful in the KNN. Some of them are Euclidean Function, Manhattan Function, Minkowski, Hamming distance, and Mahalanobis distance.

For the classification, generally, SVM (Support Vector Machine) is used. If the input is in the binary form, then the classifier has to measure the distance between the data points. The hyper lane is to be scanned to get the inputs correct and accurate. Neural networks will be used on a large basis to transfer data. The whole set up is to be put together to make the whole application.

What are the things to consider?

The performance of the application is based on various parameters such as Accuracy, sensitivity, specificity, Prevalence, Detection rate, etc. The expected error will be due to the classifiers and the data points. The error percentage should be helpful in decreasing the value.

Results and Conclusion

The results will be studied after getting proposed by the application to look for the results. The application involves knowledge about the neural networks and the classifiers. This application is useful in many fields, such as character recognition, object recognition, image segmentation, text language recognition, etc. The different classifiers should be considered separately. The application is ready to study the character.

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MNIST handwritten digit classification