Dissertation > Excellent graduate degree dissertation topics show

Several algorithm improvements nearest neighbor classification

Author: ZhongZuo
Tutor: YangJian
School: Nanjing University of Technology and Engineering
Course: Pattern Recognition and Intelligent Systems
Keywords: classification algorithm nearest neighbor K-nearest neighbor mean algorithm K-nearest neighbor linear regression algorithm pattern recognition
CLC: TP391.4
Type: Master's thesis
Year: 2012
Downloads: 229
Quote: 1
Read: Download Dissertation

Abstract


As we know, classification (or decision) is one of the most fundamental and important issues in the field of pattern recognition. And it is also a very active research subject in computer vision field. How to find an effective classification algorithm to improve pattern recognition rate has become a key issue.KNN, as a classic classification algorithm, has been widely applied to digital recognition, face recognition and so on. However, KNN also has its deficiencies; the improvement for KNN has been a hot research topic. There are many KNN variants.In general, these methods can be divided into two categories:improving the computational efficiency or enhancing the classification performance. In this thesis, we focus on the improvement of the KNN algorithms, main work is as follows:1) We review the existing pattern classification algorithms and outline the improved versions of the nearest neighbors algorithms;2) To overcome the weakness of the classical KNN, we present a K-NN mean algorithm. The presented method has been tested on the NUST603HW database and the CENPARMI database. The experimental results show that the K-NN mean algorithm outperforms the classical KNN,and it also performer better than the state-of-the-art local mean method as applied to unbalanced data;3) We present a KNN linear regression algorithm, which can take fully use of the structural information of the K nearest neighbors. The presented method has been tested on the Yale B, FKP, AR and ORL databases. The experimental results show that the proposed KNN linear regression algorithm outperforms the classical KNN, the nearest subspace method and the linear regression method.

Related Dissertations

  1. The Classification of High Dimsnsion Flew Field Based on Manifold Learning,V231.3
  2. Research on Text Classification Based on Biomimetic Pattern Recongnition,TP391.1
  3. Research on Song Emotion Classification Methods Combining Music Content and Lyrics,TP391.3
  4. Vision-based Human Movement Analysis,TP391.41
  5. Research on Content-based Trademark Image Retrieval Technology,TP391.3
  6. Research and Implementation of Liver Cancer Identification Based on Improved SVM Model,TP391.41
  7. Research and Implementation on Sintering State Prediction Method Based on SVM and PSO,TF821
  8. Traffic Incident Detection with Floating Car Data in Urban Road Networks,U491.116
  9. Research on Improved K Neighbor Support Vector Machine Algorithm Faced Text Classification,TP391.1
  10. Research on Classification and Matching for Hand Vein Image,TP391.41
  11. Classifier Design and Weight Optimization Methods Based on Multiple Views,TP18
  12. SVM Based on SIFT and scene classification,TP391.41
  13. Comprehensive Evaluation on Safety Situation of Tailing Dam Based on Mathematics,TV649
  14. The Dynamic Distributed network intrusion patterns,TP393.08
  15. Study on Privacy Preserving Classification Data Mining,TP311.13
  16. Risk Classification of Tailling Pond,TD926.4
  17. Coal Mine Shaft and Tunnel Excavation Medium-deep Hole Blasting Intelligent Base Design,TD235.33
  18. Research of the Inspection on the Electronic Button Surface Defects,TP274
  19. Research on Key Technology in Bus Realtime Information Service,TP399-C6
  20. Research on Identification System of Cashmere and Wool Fiber,TS101.921
  21. Machine Learning Algorithm-based Metaphor Recognition,TP181

CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices
© 2012 www.DissertationTopic.Net  Mobile