Chromosome classification and speech recognition using inferred Markov networks with empirical landmarks.

by Law Hon Man. === Thesis (M.Phil.)--Chinese University of Hong Kong, 1993. === Includes bibliographical references (leaves 67-70). === Chapter 1 --- Introduction --- p.1 === Chapter 2 --- Automated Chromosome Classification --- p.4 === Chapter 2.1 --- Procedures in Chromosome Classification ---...

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Other Authors: Law, Hon Man.
Format: Others
Language:English
Published: Chinese University of Hong Kong 1993
Subjects:
Online Access:http://library.cuhk.edu.hk/record=b5887802
http://repository.lib.cuhk.edu.hk/en/item/cuhk-319213
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spelling ndltd-cuhk.edu.hk-oai-cuhk-dr-cuhk_3192132019-02-19T03:49:29Z Chromosome classification and speech recognition using inferred Markov networks with empirical landmarks. Markov processes Dynamic programming Chromosomes--Classification Automatic speech recognition by Law Hon Man. Thesis (M.Phil.)--Chinese University of Hong Kong, 1993. Includes bibliographical references (leaves 67-70). Chapter 1 --- Introduction --- p.1 Chapter 2 --- Automated Chromosome Classification --- p.4 Chapter 2.1 --- Procedures in Chromosome Classification --- p.6 Chapter 2.2 --- Sample Preparation --- p.7 Chapter 2.3 --- Low Level Processing and Measurement --- p.9 Chapter 2.4 --- Feature Extraction --- p.11 Chapter 2.5 --- Classification --- p.15 Chapter 3 --- Inference of Markov Networks by Dynamic Programming --- p.17 Chapter 3.1 --- Markov Networks --- p.18 Chapter 3.2 --- String-to-String Correction --- p.19 Chapter 3.3 --- String-to-Network Alignment --- p.21 Chapter 3.4 --- Forced Landmarks in String-to-Network Alignment --- p.31 Chapter 4 --- Landmark Finding in Markov Networks --- p.34 Chapter 4.1 --- Landmark Finding without a priori Knowledge --- p.34 Chapter 4.2 --- Chromosome Profile Processing --- p.37 Chapter 4.3 --- Analysis of Chromosome Networks --- p.39 Chapter 4.4 --- Classification Results --- p.45 Chapter 5 --- Speech Recognition using Inferred Markov Networks --- p.48 Chapter 5.1 --- Linear Predictive Analysis --- p.48 Chapter 5.2 --- TIMIT Speech Database --- p.50 Chapter 5.3 --- Feature Extraction --- p.51 Chapter 5.4 --- Empirical Landmarks in Speech Networks --- p.52 Chapter 5.5 --- Classification Results --- p.55 Chapter 6 --- Conclusion --- p.57 Chapter 6.1 --- Suggested Improvements --- p.57 Chapter 6.2 --- Concluding remarks --- p.61 Appendix A --- p.63 Reference --- p.67 Chinese University of Hong Kong Law, Hon Man. Chinese University of Hong Kong Graduate School. Division of Computer Science. 1993 Text bibliography print 70 leaves : ill. ; 30 cm. cuhk:319213 http://library.cuhk.edu.hk/record=b5887802 eng Use of this resource is governed by the terms and conditions of the Creative Commons “Attribution-NonCommercial-NoDerivatives 4.0 International” License (http://creativecommons.org/licenses/by-nc-nd/4.0/) http://repository.lib.cuhk.edu.hk/en/islandora/object/cuhk%3A319213/datastream/TN/view/Chromosome%20classification%20and%20speech%20recognition%20using%20inferred%20Markov%20networks%20with%20empirical%20landmarks.jpghttp://repository.lib.cuhk.edu.hk/en/item/cuhk-319213
collection NDLTD
language English
format Others
sources NDLTD
topic Markov processes
Dynamic programming
Chromosomes--Classification
Automatic speech recognition
spellingShingle Markov processes
Dynamic programming
Chromosomes--Classification
Automatic speech recognition
Chromosome classification and speech recognition using inferred Markov networks with empirical landmarks.
description by Law Hon Man. === Thesis (M.Phil.)--Chinese University of Hong Kong, 1993. === Includes bibliographical references (leaves 67-70). === Chapter 1 --- Introduction --- p.1 === Chapter 2 --- Automated Chromosome Classification --- p.4 === Chapter 2.1 --- Procedures in Chromosome Classification --- p.6 === Chapter 2.2 --- Sample Preparation --- p.7 === Chapter 2.3 --- Low Level Processing and Measurement --- p.9 === Chapter 2.4 --- Feature Extraction --- p.11 === Chapter 2.5 --- Classification --- p.15 === Chapter 3 --- Inference of Markov Networks by Dynamic Programming --- p.17 === Chapter 3.1 --- Markov Networks --- p.18 === Chapter 3.2 --- String-to-String Correction --- p.19 === Chapter 3.3 --- String-to-Network Alignment --- p.21 === Chapter 3.4 --- Forced Landmarks in String-to-Network Alignment --- p.31 === Chapter 4 --- Landmark Finding in Markov Networks --- p.34 === Chapter 4.1 --- Landmark Finding without a priori Knowledge --- p.34 === Chapter 4.2 --- Chromosome Profile Processing --- p.37 === Chapter 4.3 --- Analysis of Chromosome Networks --- p.39 === Chapter 4.4 --- Classification Results --- p.45 === Chapter 5 --- Speech Recognition using Inferred Markov Networks --- p.48 === Chapter 5.1 --- Linear Predictive Analysis --- p.48 === Chapter 5.2 --- TIMIT Speech Database --- p.50 === Chapter 5.3 --- Feature Extraction --- p.51 === Chapter 5.4 --- Empirical Landmarks in Speech Networks --- p.52 === Chapter 5.5 --- Classification Results --- p.55 === Chapter 6 --- Conclusion --- p.57 === Chapter 6.1 --- Suggested Improvements --- p.57 === Chapter 6.2 --- Concluding remarks --- p.61 === Appendix A --- p.63 === Reference --- p.67
author2 Law, Hon Man.
author_facet Law, Hon Man.
title Chromosome classification and speech recognition using inferred Markov networks with empirical landmarks.
title_short Chromosome classification and speech recognition using inferred Markov networks with empirical landmarks.
title_full Chromosome classification and speech recognition using inferred Markov networks with empirical landmarks.
title_fullStr Chromosome classification and speech recognition using inferred Markov networks with empirical landmarks.
title_full_unstemmed Chromosome classification and speech recognition using inferred Markov networks with empirical landmarks.
title_sort chromosome classification and speech recognition using inferred markov networks with empirical landmarks.
publisher Chinese University of Hong Kong
publishDate 1993
url http://library.cuhk.edu.hk/record=b5887802
http://repository.lib.cuhk.edu.hk/en/item/cuhk-319213
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