A comparison of standard spell checking algorithms and a novel binary neural approach

In this paper, we propose a simple, flexible, and efficient hybrid spell checking methodology based upon phonetic matching, supervised learning, and associative matching in the AURA neural system. We integrate Hamming Distance and n-gram algorithms that have high recall for typing errors and a phonetic spell-checking algorithm in a single novel architecture. Our approach is suitable for any spell checking application though aimed toward isolated word error correction, particularly spell checking user queries in a search engine. We use a novel scoring scheme to integrate the retrieved words from each spelling approach and calculate an overall score for each matched word. From the overall scores, we can rank the possible matches. In this paper, we evaluate our approach against several benchmark spellchecking algorithms for recall accuracy. Our proposed hybrid methodology has the highest recall rate of the techniques evaluated. The method has a high recall rate and low-computational cost

A high performance k-NN approach using binary neural networks

This paper evaluates a novel k-nearest neighbour (k-NN) classifier built from binary neural networks. The binary neural approach uses robust encoding to map standard ordinal, categorical and numeric data sets onto a binary neural network. The binary neural network uses high speed pattern matching to recall a candidate set of matching records, which are then processed by a conventional k-NN approach to determine the k-best matches. We compare various configurations of the binary approach to a conventional approach for memory overheads, training speed, retrieval speed and retrieval accuracy. We demonstrate the superior performance with respect to speed and memory requirements of the binary approach compared to the standard approach and we pinpoint the optimal configurations. (C) 2003 Elsevier Ltd. All rights reserved

Memorias matriciales correlacionadas cuánticas, simples y mejoradas: una propuesta para su estudio y simulación sobre GPGPU

En este trabajo se desarrollan-en orden-los fundamentos de la Física Cuántica, y de la Computación Cuántica, una noción completa de las arquitecturas multicapa tolerante a fallos para la implementación física de una computadora cuántica, para completar los primeros cuatro capítulos con las técnicas propias para la simulación de este nuevo paradigma sobre placas multicore del tipo General-Purpose Computing on Graphics Processing Units (GPGPU). La segunda parte de este trabajo consiste en los tres capítulos inmediatamente siguientes, los cuales suman 10 innovaciones en este campo, a saber: 1. el Proceso de Ortogonalización Booleano (POB) con su inversa, 2. el Proceso de Ortogonalización de Gram-Schmidt Mejorado (POGSMe) con su inversa, 3. el Proceso de Ortogonalización Cuántico (POCu) con su inversa, 4. la Red Ortogonalizadora Booleana Sistólica (ROBS), 5. la Red Ortogonalizadora Cuántica Sistólica (ROCS), y 6. una métrica que llamamos Tasa Dimensional de Entrada-Salida (TDES) la cual fue creada para monitorear el impacto del mejorador para la estabilidad del Proceso Ortogonalizador de Gram-Schmidt en el costo computacional final. 7. una versión mejorada de las ya conocidas Memorias Matriciales Correlacionadas Booleanas (MMCB), es decir, la MMCB mejorada (MMCBMe) en base al innovador Proceso de Ortonormalización Booleano (POB) del Capítulo 5, 8. la Memoria Matricial Correlacionada Cuántica (MMCCu), y 9. la MMCCu Mejorada (MMCCuMe) en base al Proceso de Ortogonalización Cuántico (POCu) implementado en forma sistólica y conocida como la Red Ortogonalizadora Cuántica Sistólica (ROCS) del Capítulo 5.10. el Capítulo 7, el cual contiene las simulaciones computacionales, las cuales verifican fehacientemente la mejora en la performance de almacenamiento como resultado de aplicar el POCu a las MMCCu, así como una serie de simulaciones relativas a arreglos uni, bi y tridimensionales, los cuales representan señales, imágenes (multimediales, documentales, satelitales, biométricas, etc.) y video o bien imágenes multi e hiper-espectrales satelitales, tomografías o resonancias magnéticas seriadas, respectivamente. Dichas simulaciones tienen por objeto verificar los atributos de ortogonalización de los algoritmos desarrollados. Dado que es la primera vez que en la literatura se realizan este tipo de simulaciones en GPGPU para esta tecnología, el Capítulo 7 representa en si mismo el décimo aporte del presente trabajo a esta área del conocimiento. Un último capítulo reservado a conclusiones parciales por capítulo y generales del trabajo como un todo.Facultad de Informátic

‘Quantum’ Parallel computation with neural networks

Correlation matrix memories have been successfully applied to many domains. This work implements a production system put forward in [Austin, 2003], to demonstrate its viability as an efficient rule-chaining process. Background information on rule-chaining and CMMs is given, followed by a review of the proposed production system. Throughout the iterative development process, experimentation is performed in order to investigate the effects of changing the properties of vectors used in this system. The results show that generating vectors using the algorithm proposed in [Baum, 1988] with a weight close to log2 of the vector length provides the highest storage capacity. The simple system implemented in this work performs rule-chaining effectively. This leads to the conclusion that the proposed production system is viable, and that this area warrants further work

Matching Performance of Binary Correlation Matrix Memories

We introduce a theoretical framework for estimating the matching performance of binary correlation matrices acting as hetero-associative memories. The framework is applicable to non-recursive, fully-connected systems with binary (0,1) Hebbian weights and hard-limited threshold. It can handle both full and partial matching of single or multiple data items in non-square memories. Theoretical development takes place under a probability theory framework. Inherent uncertainties in the matching process are accommodated by the use of probability distributions to describe the numbers of correct and incorrect neuron responses during retrieval. Theoretical predictions are verified experimentally for medium-sized memories and used to aid the design of larger systems. The results highlight the Matching Performance of CMMs 2 fact that correlation-based models can act as highly efficient memories provided a small probability of retrieval error is accepted. Keywords Neural Associative Memories, Co..

Theoretical Partial and Multiple Match Performance of CMMs

We develop a framework for estimating the matching performance of binary correlation matrix memories (CMMs). The framework is applicable to non-recursive, fully-connected systems with binary (0,1) Hebbian weights. It can handle both the full and partial matching of single or multiple data items under non-sparse encoding. Development takes place under a probability theory framework. Theoretical predictions are verified experimentally for medium-sized memories and used to aid the design of a large-scale system. 1 Introduction Correlation-based models of associative memory [7, 9, 10, 13, 14] possess the advantages of biological plausibility, fast storage and retrieval, and ease of software and hardware implementation [4]. The fundamental process involved in such systems is the storage of information in weight matrices via Hebbian learning [8]. This is achieved through the generation and subsequent superimposition of the mappings between input pattern-output pattern pairs. Once storage h..