An Adaptive modular neural network with application to unconstrained character recognition

"August 1993."Includes bibliographical references (p. 24-27).Supported by the Productivity From Information Technology (PROFIT) Research Initiative at MIT.Lik Mui ... [et al.

A Knowledge based segmentation algorithm for enhanced recognition of handwritten courtesy amounts

"March 1994."Includes bibliographical references (p. [23]-[24]).Supported by the Productivity From Information Technology (PROFIT) Research Initiative at MIT.Karim Hussein ... [et al.

Design for novel enhanced weightless neural network and multi-classifier.

Weightless neural systems have often struggles in terms of speed, performances, and memory issues. There is also lack of sufficient interfacing of weightless neural systems to others systems. Addressing these issues motivates and forms the aims and objectives of this thesis. In addressing these issues, algorithms are formulated, classifiers, and multi-classifiers are designed, and hardware design of classifier are also reported. Specifically, the purpose of this thesis is to report on the algorithms and designs of weightless neural systems. A background material for the research is a weightless neural network known as Probabilistic Convergent Network (PCN). By introducing two new and different interfacing method, the word "Enhanced" is added to PCN thereby giving it the name Enhanced Probabilistic Convergent Network (EPCN). To solve the problem of speed and performances when large-class databases are employed in data analysis, multi-classifiers are designed whose composition vary depending on problem complexity. It also leads to the introduction of a novel gating function with application of EPCN as an intelligent combiner. For databases which are not very large, single classifiers suffices. Speed and ease of application in adverse condition were considered as improvement which has led to the design of EPCN in hardware. A novel hashing function is implemented and tested on hardware-based EPCN. Results obtained have indicated the utility of employing weightless neural systems. The results obtained also indicate significant new possible areas of application of weightless neural systems

Comparative Study of Neural Networks Algorithms for Cloud Computing CPU Scheduling

Cloud Computing is the most powerful computing model of our time. While the major IT providers and consumers are competing to exploit the benefits of this computing model in order to thrive their profits, most of the cloud computing platforms are still built on operating systems that uses basic CPU (Core Processing Unit) scheduling algorithms that lacks the intelligence needed for such innovative computing model. Correspdondingly, this paper presents the benefits of applying Artificial Neural Networks algorithms in regards to enhancing CPU scheduling for Cloud Computing model. Furthermore, a set of characteristics and theoretical metrics are proposed for the sake of comparing the different Artificial Neural Networks algorithms and finding the most accurate algorithm for Cloud Computing CPU Scheduling

The synthesis of artificial neural networks using single string evolutionary techniques.

The research presented in this thesis is concerned with optimising the structure of Artificial Neural Networks. These techniques are based on computer modelling of biological evolution or foetal development. They are known as Evolutionary, Genetic or Embryological methods. Specifically, Embryological techniques are used to grow Artificial Neural Network topologies. The Embryological Algorithm is an alternative to the popular Genetic Algorithm, which is widely used to achieve similar results. The algorithm grows in the sense that the network structure is added to incrementally and thus changes from a simple form to a more complex form. This is unlike the Genetic Algorithm, which causes the structure of the network to evolve in an unstructured or random way. The thesis outlines the following original work: The operation of the Embryological Algorithm is described and compared with the Genetic Algorithm. The results of an exhaustive literature search in the subject area are reported. The growth strategies which may be used to evolve Artificial Neural Network structure are listed. These growth strategies are integrated into an algorithm for network growth. Experimental results obtained from using such a system are described and there is a discussion of the applications of the approach. Consideration is given of the advantages and disadvantages of this technique and suggestions are made for future work in the area. A new learning algorithm based on Taguchi methods is also described. The report concludes that the method of incremental growth is a useful and powerful technique for defining neural network structures and is more efficient than its alternatives. Recommendations are also made with regard to the types of network to which this approach is best suited. Finally, the report contains a discussion of two important aspects of Genetic or Evolutionary techniques related to the above. These are Modular networks (and their synthesis) and the functionality of the network itself

From Data Topology to a Modular Classifier

This article describes an approach to designing a distributed and modular neural classifier. This approach introduces a new hierarchical clustering that enables one to determine reliable regions in the representation space by exploiting supervised information. A multilayer perceptron is then associated with each of these detected clusters and charged with recognizing elements of the associated cluster while rejecting all others. The obtained global classifier is comprised of a set of cooperating neural networks and completed by a K-nearest neighbor classifier charged with treating elements rejected by all the neural networks. Experimental results for the handwritten digit recognition problem and comparison with neural and statistical nonmodular classifiers are given

High-Quality Wavelets Features Extraction for Handwritten Arabic Numerals Recognition

Arabic handwritten digit recognition is the science of recognition and classification of handwritten Arabic digits. It has been a subject of research for many years with rich literature available on the subject.  Handwritten digits written by different people are not of the same size, thickness, style, position or orientation. Hence, many different challenges have to overcome for resolving the problem of handwritten digit recognition.  The variation in the digits is due to the writing styles of different people which can differ significantly.  Automatic handwritten digit recognition has wide application such as automatic processing of bank cheques, postal addresses, and tax forms. A typical handwritten digit recognition application consists of three main stages namely features extraction, features selection, and classification. One of the most important problems is feature extraction. In this paper, a novel feature extraction approach for off-line handwritten digit recognition is presented. Wavelets-based analysis of image data is carried out for feature extraction, and then classification is performed using various classifiers. To further reduce the size of training data-set, high entropy subbands are selected. To increase the recognition rate, individual subbands providing high classification accuracies are selected from the over-complete tree. The features extracted are also normalized to standardize the range of independent variables before providing them to the classifier. Classification is carried out using k-NN and SVMs. The results show that the quality of extracted features is high as almost equivalently high classification accuracies are acquired for both classifiers, i.e. k-NNs and SVMs

An Integrated architecture for recognition of totally unconstrained handwritten numerals

Reprint. Reprinted from the International journal of pattern recognition and artificial intelligence. Vol. 7, no. 4 (1993) "January 1993."Includes bibliographical references (p. 127-128).Supported by the Productivity From Information Technology (PROFIT) Research Initiative at MIT.Amar Gupta ... [et al.

The Microsoft 2017 Conversational Speech Recognition System

We describe the 2017 version of Microsoft's conversational speech recognition system, in which we update our 2016 system with recent developments in neural-network-based acoustic and language modeling to further advance the state of the art on the Switchboard speech recognition task. The system adds a CNN-BLSTM acoustic model to the set of model architectures we combined previously, and includes character-based and dialog session aware LSTM language models in rescoring. For system combination we adopt a two-stage approach, whereby subsets of acoustic models are first combined at the senone/frame level, followed by a word-level voting via confusion networks. We also added a confusion network rescoring step after system combination. The resulting system yields a 5.1\% word error rate on the 2000 Switchboard evaluation set