{"id":12939,"date":"2021-11-30T19:20:45","date_gmt":"2021-12-01T00:20:45","guid":{"rendered":"https:\/\/carleton.ca\/scs\/?page_id=12939"},"modified":"2021-11-30T19:20:45","modified_gmt":"2021-12-01T00:20:45","slug":"tr-97-09-designing-syntactic-pattern-classifiers-using-vector-quantization-and-parametric-string-editing","status":"publish","type":"page","link":"https:\/\/carleton.ca\/scs\/research\/scs-technical-reports\/technical-reports-1997\/tr-97-09-designing-syntactic-pattern-classifiers-using-vector-quantization-and-parametric-string-editing\/","title":{"rendered":"TR-97-09: Designing Syntactic Pattern Classifiers Using Vector Quantization and Parametric String Editing"},"content":{"rendered":"

Carleton University
\nTechnical Report<\/a> TR-97-09
\nMay 1997<\/p>\n

TR-97-09: Designing Syntactic Pattern Classifiers Using Vector Quantization and Parametric String Editing<\/h2>\n
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B. John Oommen & R.K.S. Loke<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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Abstract<\/h3>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

We consider a fundamental problem in Syntactic Pattern Recognition (PR) in which we are required to recognize a string from its noisy version. We assume that the system has a dictionary which is a collection of all the ideal representations of the objects in question. When a noisy sample has to be processed, the system compares it with every element in the dictionary based on a nearest-neighbor philosophy. This is typically achieved using three standard edit operations -substitution, insertion and deletion. To accomplish this, one usually assigns a distance for the elementary symbol operations, d(., .), and the inter-pattern distance, D(., .), is computed as a function of these symbol edit distances. In this paper we consider the assignment of the inter\u00adsymbol distances in terms of the novel and interesting assignments -the parametric distances – recently introduced by Bunke et al. [4]. We show how the classifier can be trained to get the optimal parametric distance using vector quantization in the meta-space, and report classification results after such a training process. In all our experiments, the training was typically achieved in a very few iterations. The subsequent classification accuracy we obtained using this single\u00adparameter scheme was 96.13 %. The power of the scheme is obvious if we compare it to 96.67%, which is the accuracy of the scheme which uses the complete array of inter-symbol distances derived from a knowledge of all the confusion probabilities.<\/p>\n

TR-97-09.pdf<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

Carleton University Technical Report TR-97-09 May 1997 TR-97-09: Designing Syntactic Pattern Classifiers Using Vector Quantization and Parametric String Editing B. John Oommen & R.K.S. Loke Abstract We consider a fundamental problem in Syntactic Pattern Recognition (PR) in which we are required to recognize a string from its noisy version. We assume that the system has […]<\/p>\n","protected":false},"author":49,"featured_media":0,"parent":12157,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_relevanssi_hide_post":"","_relevanssi_hide_content":"","_relevanssi_pin_for_all":"","_relevanssi_pin_keywords":"","_relevanssi_unpin_keywords":"","_relevanssi_related_keywords":"","_relevanssi_related_include_ids":"","_relevanssi_related_exclude_ids":"","_relevanssi_related_no_append":"","_relevanssi_related_not_related":"","_relevanssi_related_posts":"","_relevanssi_noindex_reason":"","_mi_skip_tracking":false,"_exactmetrics_sitenote_active":false,"_exactmetrics_sitenote_note":"","_exactmetrics_sitenote_category":0,"footnotes":"","_links_to":"","_links_to_target":""},"yoast_head":"\nTR-97-09: Designing Syntactic Pattern Classifiers Using Vector Quantization and Parametric String Editing - School of Computer Science<\/title>\n<meta name=\"description\" content=\"Carleton University Technical Report TR-97-09 May 1997 TR-97-09: Designing Syntactic Pattern Classifiers Using Vector Quantization and Parametric String\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/carleton.ca\/scs\/research\/scs-technical-reports\/technical-reports-1997\/tr-97-09-designing-syntactic-pattern-classifiers-using-vector-quantization-and-parametric-string-editing\/\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"1 minute\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/carleton.ca\/scs\/research\/scs-technical-reports\/technical-reports-1997\/tr-97-09-designing-syntactic-pattern-classifiers-using-vector-quantization-and-parametric-string-editing\/\",\"url\":\"https:\/\/carleton.ca\/scs\/research\/scs-technical-reports\/technical-reports-1997\/tr-97-09-designing-syntactic-pattern-classifiers-using-vector-quantization-and-parametric-string-editing\/\",\"name\":\"TR-97-09: Designing Syntactic Pattern Classifiers Using Vector Quantization and Parametric String Editing - 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