๐”– Scriptorium
โœฆ   LIBER   โœฆ

๐Ÿ“

Neural Networks for Vision, Speech and Natural Language

โœ Scribed by C. Nightingale, D. J. Myers, R. Linggard (auth.), R. Linggard, D. J. Myers, C. Nightingale (eds.)


Publisher
Springer Netherlands
Year
1992
Tongue
English
Leaves
449
Series
BT Telecommunications Series 1
Edition
1
Category
Library

โฌ‡  Acquire This Volume

No coin nor oath required. For personal study only.

โœฆ Synopsis


This book is a collection of chapters describing work carried out as part of a large project at BT Laboratories to study the application of connectionist methods to problems in vision, speech and natural language processing. Also, since the theoretical formulation and the hardware realization of neural networks are significant tasks in themselves, these problems too were addressed. The book, therefore, is divided into five Parts, reporting results in vision, speech, natural language, hardware implementation and network architectures. The three editors of this book have, at one time or another, been involved in planning and running the connectionist project. From the outset, we were concerned to involve the academic community as widely as possible, and consequently, in its first year, over thirty university research groups were funded for small scale studies on the various topics. Co-ordinating such a widely spread project was no small task, and in order to concentrate minds and resources, sets of test problems were devised which were typical of the application areas and were difficult enough to be worthy of study. These are described in the text, and constitute one of the successes of the project.

โœฆ Table of Contents


Front Matter....Pages i-xii
Introduction Neural Networks for Vision, Speech and Natural Language....Pages 1-4
Neutral Networks for Vision: An Introduction....Pages 5-11
Image Feature Location in Multi-Resolution Images Using a Hierarchy of Multilayer Perceptrons....Pages 13-29
Training Multilayer Perceptrons for Facial Feature Location: A Case Study....Pages 30-49
The Detection of Eyes in Facial Images Using Radial Basis Functions....Pages 50-64
A Neural Network Feature Detector Using a Multi-Resolution Pyramid....Pages 65-92
Training and Testing of Neural Net Window Operators on Spatiotemporal Image Sequences....Pages 93-111
Image Classification Using Gabor Representations with a Neural Net....Pages 112-127
Neural Networks for Speech Processing: An Introduction....Pages 129-134
Spoken Alphabet Recognition Using Multilayer Perceptrons....Pages 135-147
Speaker Independent Vowel Recognition....Pages 148-159
Dissection of Perceptron Structures in Speech and Speaker Recognition....Pages 160-176
Segmental Sub-Word Unit Classification Using a Multilayer Perceptron....Pages 177-192
Connectionist Natural Language Processing: An Introduction....Pages 193-201
A Single Layer Higher Order Neural Net and its Applications to Context Free Grammar Recognition....Pages 203-234
Functional Compositionality and Soft Preference Rules....Pages 235-255
Applications of Multilayer Perceptrons in Text-To-Speech Synthesis Systems....Pages 256-288
Hardware Implementation of Neural Networks: An Introduction....Pages 289-292
Finite Wordlength, Integer Arithmetic Multilayer Perceptron Modelling for Hardware Realization....Pages 293-311
A VLSI Architecture for Implementing Neural Networks with On-Chip Backpropagation Learning....Pages 312-329
An Opto-Electronic Neural Network Processor....Pages 330-347
Architectures: An Introduction....Pages 349-352
A Dynamic Topology Net....Pages 353-369
The Stochastic Search Network....Pages 370-387
Node Sequence Networks....Pages 388-409
Some Dynamical Properties of Neural Networks....Pages 410-438
Back Matter....Pages 439-442

โœฆ Subjects


Signal, Image and Speech Processing


๐Ÿ“œ SIMILAR VOLUMES


Neural Networks for Natural Language Pro
โœ Sumathi S., Janani M ๐Ÿ“‚ Library ๐Ÿ“… 2019 ๐Ÿ› Engineering Science Reference ๐ŸŒ English

Information in today's advancing world is rapidly expanding and becoming widely available. This eruption of data has made handling it a daunting and time-consuming task. Natural language processing (NLP) is a method that applies linguistics and algorithms to large amounts of this data to make it mor

Neural Network Methods for Natural Langu
โœ Yoav Goldberg ๐Ÿ“‚ Library ๐Ÿ“… 2017 ๐Ÿ› Morgan & Claypool ๐ŸŒ English

Neural networks are a family of powerful machine learning models. This book focuses on the application of neural network models to natural language data. The first half of the book (Parts I and II) covers the basics of supervised machine learning and feed-forward neural networks, the basics of worki

Neural network methods for natural langu
โœ Goldberg, Yoav ๐Ÿ“‚ Library ๐Ÿ“… 2017 ๐Ÿ› Morgan & Claypool ๐ŸŒ English

Neural networks are a family of powerful machine learning models. This book focuses on the application of neural network models to natural language data. The first half of the book (Parts I and II) covers the basics of supervised machine learning and feed-forward neural networks, the basics of worki

Deep Learning Approach for Natural Langu
โœ Kumar, L. Ashok;Renuka, D. Karthika; D. Karthika Renuka ๐Ÿ“‚ Library ๐Ÿ“… 2023 ๐Ÿ› Taylor & Francis Group ๐ŸŒ English

Deep Learning Approach for Natural Language Processing, Speech, and Computer Vision provides an overview of general deep learning methodology and its applications of natural language processing (NLP), speech and computer vision tasks. It simplifies and presents the concepts of deep learning in a com

cover
โœ Carpenter, Gail A ๐Ÿ“‚ Library ๐Ÿ“… 1992 ๐Ÿ› Cambridge, Mass. : MIT Press ๐ŸŒ English

This interdisciplinary survey brings together recent models and experiments on how the brain sees and learns to recognize objects. It shows how to use these insights in technology and describes how neural networks provide a unifying computational framework for reaching these goals. Several chapters