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Optimization Method of SVM Classifier for Gene Microarray Data
Author: FengQing
Tutor: WeiJinMao
School: Northeast Normal University
Course: Circuits and Systems
Keywords: Support Vector Machine Dimensionality reduction Microarray Principal Component Analysis Multidimensional scaling transformation Locally Linear Embedding Laplace feature map
CLC: Q75
Type: Master's thesis
Year: 2010
Downloads: 66
Quote: 1
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Abstract
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Support Vector Machine is a new learning methods , developed on the basis of the statistical learning theory is one of the most popular classification techniques . Compared with the other learning algorithms , support vector machines using structural risk minimization principle , can better solve the small sample learning problems , especially for the high number of dimensions , the small sample size , nonlinear microarray data . DNA microarrays load thousands of DNA fragments , which produce large amounts of microarray data , research and analysis of the auxiliary diagnosis and treatment of diseases with high medical value . With the increasing of the microarray information processing and information mining technology development , support vector machines as a potential data mining technology has become an important direction for the study of gene chip . Around this topic , the paper mainly do the work of the following areas : (1) the curse of dimensionality of microarray data , the paper will be designed to support vector machine integration with a variety of dimensionality reduction method optimization method . Study the impact of a variety of dimensionality reduction method to improve the performance of the support vector machine classifier . (2) five cancer data dimensionality reduction analysis and SVM classification results is given . The results of the microarray data classification by linear and nonlinear dimensionality reduction method to verify the potential of the non-linear structure of high -dimensional biological data . ( 3) using two linear (PCA, MDS) and three nonlinear dimensionality reduction methods (Isomap, LLE, LEM) and visual projection dimensionality reduction data in low-dimensional space , while the low-dimensional space clustering assessment .
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CLC: > Biological Sciences > Molecular Biology > Molecular Genetics
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