Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

Recurrent neural networks are powerful sequence learners. They are able to incorporate context information in a flexible way, and are robust to localised distortions of the input data. These properties make them well suited to sequence labelling, where input sequences are transcribed with streams of labels. The aim of this thesis is to advance the state-of-the-art in supervised sequence labelling with recurrent networks. Its two main contributions are (1) a new type of output layer that allows recurrent networks to be trained directly for sequence labelling tasks where the alignment between the inputs and the labels is unknown, and (2) an extension of the long short-term memory network architecture to multidimensional data, such as images and video sequences.

https://mediatum.ub.tum.de/doc/673554/file.pdf

Supervised Sequence Labelling with Recurrent Neural Networks

Formation and control of optimal trajectory in human multijoint arm movement : minimum torque-change model

Audio- and video-based biometric person authentication

In this paper we explore a new model focused on integrating two classifiers; Convolutional Neural Network (CNN) and Support Vector Machine (SVM) for offline Arabic handwriting recognition (OAHR) on which the dropout technique was applied. The suggested system altered the trainable classifier of the CNN by the SVM classifier. A convolutional network is beneficial for extracting features information and SVM functions as a recognizer. It was found that this model both automatically extracts features from the raw images and performs classification. Additionally, we protected our model against over-fitting due to the powerful performance of dropout. In this work, the recognition on the handwritten Arabic characters was evaluated; the training and test sets were taken from the HACDB and IFN/ENIT databases. Simulation results proved that the new design based-SVM of the CNN classifier architecture with dropout performs significantly more efficiently than CNN based-SVM model without dropout and the standard CNN classifier. The performance of our model is compared with character recognition accuracies gained from state-of-the-art Arabic Optical Character Recognition, producing favorable results.

/pdf/a-new-design-based-svm-of-the-cnn-classifier-architecture-6uq9iimqlj.pdf

A New Design Based-SVM of the CNN Classifier Architecture with Dropout for Offline Arabic Handwritten Recognition

On-line handwritten digit recognition based on trajectory and velocity modeling

Researches on handwriting recognition have known a great attention since it has been considered as a technological revolution in man-machines interfaces especially that handwriting has continued to persist as the most used mean of communication and recording information in day-to-day life. The challenging nature of handwriting recognition and segmentation has attracted the attention of researchers from academic and industry circles. The huge part of these researches deals with Latin and Chinese. Interest in Arabic script comes years later, and so the state of the art is less advanced. This survey describes the nature of this Arabic handwritten language and the basic concepts behind the recognition process. An overview of the state of the art of online Arabic handwriting recognition is presented. It is based on an extensive review of the literature in order to describe background in the field, discussion of the methods, and future research directions. It is the first survey to focus on online Arabic handwriting recognition and provide recognition rates and descriptions of database used for the discussed approaches.

https://link.springer.com/content/pdf/10.1007%2Fs10032-012-0186-8.pdf

Online Arabic handwriting recognition: a survey

On-line Arabic handwriting recognition system based on visual encoding and genetic algorithm

Arabic script presents a challenge complexity and variability for handwriting recognition. The first on line Arabic Database called ADAB is known as a standard benchmark in the ICDAR competition of 2009. This paper describes the Online Arabic handwriting recognition competition held at ICDAR 2011. 3 groups with 5 systems are participating in the competition. The systems were tested on known data (sets 1 to 4) and on two test datasets which are unknown to all participants (set 5 and set 6). The systems are compared on the most important characteristic of classification systems, the recognition rate. Additionally, the relative speed of every system was compared. A short description of the participating groups, their systems, the experimental setup, and the performed results are presented.

http://www.cvc.uab.es/icdar2009/documents/Proposal_Online%20Arabic%20Handwriting%20Recognition%20Competition_ICDAR2009_version_1.1.pdf

Monji Kherallah

Papers

A New Design Based-SVM of the CNN Classifier Architecture with Dropout for Offline Arabic Handwritten Recognition

On-line handwritten digit recognition based on trajectory and velocity modeling

Online Arabic handwriting recognition: a survey

On-line Arabic handwriting recognition system based on visual encoding and genetic algorithm

Online Arabic Handwriting Recognition Competition