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This monograph, "An Adaptive Weighting Conclusion for Limited Dataset Affidavit Problems" by Dan, Chen, 陳丹, was obtained from The Institution of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Mess halls: Attribution 3. 0 Hong Kong License. The content of this monograph has not been altered in any way. We have altered the formatting in warn to facilitate the ease of distribution and reading of the monograph. All compensates for not granted by the above license are retained by the reporter. Abstract: Abstract of thesis entitled "An adaptive weighting conclusion for limited dataset affidavit problems" Submitted by Dan Chen for the degree of Comprehend of Ideology at The Institution of Hong Kong in Aug 2005 For pattern classification, traditional algorithms are mostly based on sta- tistical theories that assurance excellent results when the buildup good set is big enough. However, in many real heavenly body applications the buildup good set is usu- friend small. Some of these small good set problems also demand such a low flaw rate that user intercommunications are involved in the classification industry. In the light of this situation, this case proposes an adaptive weighting conclusion (AWA) which takes full convenience of human inspectors' industry to improve classification accuracy in general affidavit problems. The conclusion uses human expert inspectors' judgment for confused end samples. At the same stage, the judgment is fed back to the affidavit sys- tem to boost performance. The base classifiers using local information and global information cooperate with each other to decide the place insignia and the essential of re-examination. Because the re-examined samples are not indepen- dently identically distributed, special treatments are needed to unite them into the buildup dataset. Therefore we proposed the likelihood weighted average k-nn (LWAKNN) and weighted Trawler linear discriminant (WFLD) as the base classifiers. We assign different ball and chains to different buildup samples in LWAKNN according to their distances from the place miserable. There is a direct correlationbetween the frigidity and the within place thickness. As a result, the thickness distri- bution information is conducted in the limited LWAKNN buildup samples. The leaning due to the buildup dataset sparseness is mitigated, reducing the flaw rate significantly. The weighting scheme in WFLD takes into account the proportion of the re-examined samples to all the samples in that place, thus constraining the influence of the confused end samples in deciding place restrictions. A strategy using AWA is presented with empirically determined restrictions. Results confirmed by simulations and real heavenly body good probes show that the adaptive weighting conclusion can perform much better in limited good set affidavit problems. AWA also outperforms other classifiers in multi-place clas- sification problems. The way to adjust the restrictions has been proposed and tested with simulated good. An abstract of exactly 331 words. DOI: 10. 5353/th_b3204897 Subjects: Pattern recognition systemsAlgorithmsClassification - Mathematical set examples.

An Adaptive Weighting Algorithm for Limited Dataset Verification Problems read online or download

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  • Publisher: Open Dissertation Press
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  • Cover: Hardcover
  • Language: English
  • ISBN-10: 136112993X
  • ISBN-13: 978-1361129937
  • Dimensions: 8.5 x 0.3 x 11 inches
  • Weight: 1.2 pounds
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  • Price: $59.00

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