東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Reliable Pattern Recognition System ...

He, Chun Lei.

FindBook

Google Book

Amazon

博客來

Reliable Pattern Recognition System with Novel Semi-Supervised Learning Approach.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Reliable Pattern Recognition System with Novel Semi-Supervised Learning Approach./
作者:	He, Chun Lei.
面頁冊數:	123 p.
附註:	Source: Dissertation Abstracts International, Volume: 72-05, Section: B, page: .
Contained By:	Dissertation Abstracts International72-05B.
標題:	Engineering, Computer. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=NR71138
ISBN:	9780494711385

Reliable Pattern Recognition System with Novel Semi-Supervised Learning Approach.
He, Chun Lei.

Reliable Pattern Recognition System with Novel Semi-Supervised Learning Approach. - 123 p.

Source: Dissertation Abstracts International, Volume: 72-05, Section: B, page: .

Thesis (Ph.D.)--Concordia University (Canada), 2010.

Over the past decade, there has been considerable progress in the design of statistical machine learning strategies, including Semi-Supervised Learning (SSL) approaches. However, researchers still have difficulties in applying most of these learning strategies when two or more classes overlap, and/or when each class has a bimodal/multimodal distribution.

ISBN: 9780494711385Subjects--Topical Terms:

1669061
Engineering, Computer.

Reliable Pattern Recognition System with Novel Semi-Supervised Learning Approach.
LDR:03315nam 2200289 4500 001 1403334
005 20111115085212.5
008 130515s2010 ||||||||||||||||| ||eng d
020 $a 9780494711385
035 $a (UMI)AAINR71138
035 $a AAINR71138
040 $a UMI $c UMI
100 1 $a He, Chun Lei. $3 1682592
245 1 0 $a Reliable Pattern Recognition System with Novel Semi-Supervised Learning Approach.
300 $a 123 p.
500 $a Source: Dissertation Abstracts International, Volume: 72-05, Section: B, page: .
502 $a Thesis (Ph.D.)--Concordia University (Canada), 2010.
520 $a Over the past decade, there has been considerable progress in the design of statistical machine learning strategies, including Semi-Supervised Learning (SSL) approaches. However, researchers still have difficulties in applying most of these learning strategies when two or more classes overlap, and/or when each class has a bimodal/multimodal distribution.
520 $a In this thesis, an efficient, robust, and reliable recognition system with a novel SSL scheme has been developed to overcome overlapping problems between two classes and bimodal distribution within each class. This system was based on the nature of category learning and recognition to enhance the system's performance in relevant applications. In the training procedure, besides the supervised learning strategy, the unsupervised learning approach was applied to retrieve the "extra information" that could not be obtained from the images themselves. This approach was very helpful for the classification between two confusing classes. In this SSL scheme, both the training data and the test data were utilized in the final classification.
520 $a In this thesis, the design of a promising supervised learning model with advanced state-of-the-art technologies is firstly presented, and a novel rejection measurement for verification of rejected samples, namely Linear Discriminant Analysis Measurement (LDAM), is defined. Experiments on CENPARMI's Hindu-Arabic Handwritten Numeral Database, CENPARMI's Numerals Database, and NIST's Numerals Database were conducted in order to evaluate the efficiency of LDAM.
520 $a Moreover, multiple verification modules, including a Writing Style Verification (WSV) module, have been developed according to four newly defined error categories. The error categorization was based on the different costs of misclassification. The WSV module has been developed by the unsupervised learning approach to automatically retrieve the person's writing styles so that the rejected samples can be classified and verified accordingly.
520 $a As a result, errors on CENPARMI's Hindu-Arabic Handwritten Numeral Database (24,784 training samples, 6,199 testing samples) were reduced drastically from 397 to 59, and the final recognition rate of this HAHNR reached 99.05%, a significantly higher rate compared to other experiments on the same database. When the rejection option was applied on this database, the recognition rate, error rate, and reliability were 97.89%, 0.63%, and 99.28%, respectively.
590 $a School code: 0228.
650 4 $a Engineering, Computer. $3 1669061
690 $a 0464
710 2 $a Concordia University (Canada). $3 1018569
773 0 $t Dissertation Abstracts International $g 72-05B.
790 $a 0228
791 $a Ph.D.
792 $a 2010
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=NR71138