کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
531974 | 869892 | 2006 | 15 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Class-dependent PCA, MDC and LDA: A combined classifier for pattern classification
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
چشم انداز کامپیوتر و تشخیص الگو
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![عکس صفحه اول مقاله: Class-dependent PCA, MDC and LDA: A combined classifier for pattern classification Class-dependent PCA, MDC and LDA: A combined classifier for pattern classification](/preview/png/531974.png)
چکیده انگلیسی
Several pattern classifiers give high classification accuracy but their storage requirements and processing time are severely expensive. On the other hand, some classifiers require very low storage requirement and processing time but their classification accuracy is not satisfactory. In either of the cases the performance of the classifier is poor. In this paper, we have presented a technique based on the combination of minimum distance classifier (MDC), class-dependent principal component analysis (PCA) and linear discriminant analysis (LDA) which gives improved performance as compared with other standard techniques when experimented on several machine learning corpuses.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition - Volume 39, Issue 7, July 2006, Pages 1215–1229
Journal: Pattern Recognition - Volume 39, Issue 7, July 2006, Pages 1215–1229
نویسندگان
Alok Sharma, Kuldip K. Paliwal, Godfrey C. Onwubolu,