A short survey of existing methods of rule extraction from neural networks starts the chapter. Because searching rules is similar to NP-hard problem it justifies an application of evolutionary algorithm to the rule extraction. The survey contains a short description of evolutionary based methods, as well. It creates a background to show own experiences from satisfying applications of evolutionary algorithms to this process. Two methods of rule extraction namely: REX and GEX are presented in details. They represent a global approach to rule extraction, perceiving a neural network by the set of pairs: input pattern and response produced by the neural network. REX uses prepositional fuzzy rules and is composed of two methods REX Michigan and REX Pitt. GEX takes an advantage of classical crisp rules. All details of these methods are described in the chapter. Their efficiency was tested in experimental studies using different benchmark data sets from UCI repository. The comparison to other existing methods was made and is presented in the chapter.

Evolutionary Approaches to Rule Extraction from Neural Networks

The identification of hand-written digits is among the most significant issue in the applications for pattern detection. In many application such as postal code, check online routing bank accounts, data form entry, etc., the applications of digits recognition include the center of the issue is the need to construct an appropriate algorithm that can identify hand-written digits and that users upload through a smartphone and scanner and other digital devices. In this paper, we took a repository of MNIST, which is a sub-set of the database of NIST results. The MNIST dataset accommodates the collection of hand-written scanned images from a broader variety of NIST repository produced by hand. The method proposed in this paper is centered on numerous machine learning methods to perform hand-written digit detection that is off-line in the python language platform. The primary objective of this paper is to render hand-written digits recognition reliable and precise. For the identification of digits using MNIST many machine learning algorithms have been used including Support Vector Machine, Multilayer Perceptron, Decision Tree, Naïve Bayes, K-Nearest Neighbor, and Random Forest.

Handwritten Digits Identification Using Mnist Database Via Machine Learning Models

This paper presents an approach to digit recognition using single layer neural network classifier with Principal Component Analysis (PCA) The handwritten digit recognition is an important area of research as there are so many applications which are using handwritten recognition and it can also be applied to new application There are many algorithms applied to this computer vision problem and many more algorithms are continuously developed on this to make the handwritten recognition classify digits more accurately with less computation involved The proposed model in this paper aims to reduce the features to reduce computation requirements and successfully classify the digit into 10 categories (0 to 9) The system designed consists of backward propagation (BP) neural network and is trained and tested on the MNIST dataset of handwritten digit The proposed system was able to obtain 9839% accuracy on the MNIST 10,000 test dataset The Principal Component Analysis (PCA) is used for feature extraction to curtail the computational and training time and at the same time produce high accuracy It was clearly observed that the training time is reduced by up to 80% depending on the number of principal component selected We will consider not only the accuracy, but also the training time, recognition time and memory requirements for entire process Further, we identified the digits which were misclassified by the algorithm Finally, we generate our own test dataset and predict the labels using this system

Digit recognition using single layer neural network with principal component analysis

In this paper we propose a new Handwritten Signature Recognition Algorithm. The Algorithm is based on pixel-to-pixel relationship between Images. The Algorithms are based on extensive statistical analysis, Standard Deviation, variance and Theory of Cross-Correlation. This is an extension work of Handwritten Signature Identification. This Algorithm supports the application environment and we strongly believe that "User Recognition" could be a solid platform for future research and study based on statistics and probability theory.

Handwritten signature recognition using departure of images from independence

Machine learning and deep learning plays an important role in computer technology and artificial intelligence. With the use of deep learning and machine learning, human effort can be reduce in recognizing, learning, predictions and many more areas. This paper presents recognizing the handwritten digits (0 to 9) from the famous MNIST dataset, comparing classifiers like KNN, PSVM, NN and convolution neural network on basis of performance, accuracy, time, sensitivity, positive productivity, and specificity with using different parameters with the classifiers. Keywords— Handwritten Digits, Vector Machine, Neural Networks, Convolution, Machine Learning

Survey on Handwritten Digit Recognition using Machine Learning

A handwritten digit recognition system was used in a demonstration project to visualize artificial neural networks, in particular Kohonen's self-organizing feature map. The purpose of this project was to introduce neural networks through a relatively easy-to-understand application to the general public. This paper describes several techniques used for preprocessing the handwritten digits, as well as a number of ways in which neural networks were used for the recognition task. Whereas the main goal was a purely educational one, a moderate recognition rate of 98% was reached on a test set.

Berend-Jan van der Zwaag

Papers

Handwritten Digit Recognition: A Neural Network Demo