[Research on individual sleep staging based on principal component analysis and support vector machine].
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Face recognition systems have enhanced human-computer interactions in the last ten years. However, the literature reveals that current techniques used for identifying or verifying faces are not immune to limitations. Principal Component Analysis-Support Vector Machine (PCA-SVM) and Principal Component Analysis-Artificial Neural Network (PCA-ANN) are among the relatively recent and powerful face analysis techniques. Compared to PCA-ANN, PCA-SVM has demonstrated generalization capabilities in many tasks, including the ability to recognize objects with small or large data samples. Apart from requiring a minimal number of parameters in face detection, PCA-SVM minimizes generalization errors and avoids overfitting problems better than PCA-ANN. PCA-SVM, however, is ineffective and inefficient in detecting human faces in cases in which there is poor lighting, long hair, or items covering the subject's face. This study proposes a novel PCA-SVM-based model to overcome the recognition problem of PCA-ANN and enhance face detection. The experimental results indicate that the proposed model provides a better face recognition outcome than PCA-SVM.
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본 논문에서는 PCA/SVM(Support Vector Machine)을 이용한 새로운 얼굴인식 방법을 제안한다. SVM은 벡터공간에서 임의의 비선형 경계를 찾아 두 개의 집합을 분류하는 방법으로, 주어진 조건하에서 수학적으로 최적의 해를 찾을 수 있다고 알려져 있다. 식별과정 중에는 먼저 주성분분석(PCA, Principal Component Analysis)을 이용하여 얼굴을 특징추출하고 새로운 학습 얼굴영상과 테스트 얼굴영상이 입력되면 고유벡터로 만드는 특징공간에 대한 사영을 취하여 구하는 고유얼굴 성분값을 SVM를 이용하여 얼굴을 인식한다. 분석된 결과를 종합해 볼 때, PCA/SVM을 이용한 방법은 단순히 PCA만을 이용한 방법에 비해 더 나은 얼굴 인식률을 보이다.
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Face recognition is an important research field of pattern recognition.Up to now,it caused researchers great concern from these fields,such as pattern recognition,computer vision,and physiology,and so on.Various recognition algorithms have been proposed. Generally,we can make sure that the performance of face recognition system is determined by how to extract feature vector exactly and to classify them into a class accurately.Therefore,it is necessary for us to pay close attention to feature extractor and classifier.In this paper, in order to raise recognition rate,Principle Component Analysis (PCA) is used to extract image feature,and Support Vector Machine (SVM) is used to deal with face recognition problem. SVM has been recently proposed as a new classifier for pattern recognition.We take Principle Component Analysis & Support Vector Machine (PCA&SVM) to do experiments on the Cambridge ORL Face database,and compare this method with Principle Component Analysis & Nearest Neighbor (PCA&NN) and Support Vector Machine (SVM) on recognition rate and recognition time respectively.Finally,this experimental results show that recognition rate of this method,under small samples circumstance,is better than other two methods. It shows that,for face recognition,sending PCA features to SVM classifiers is feasible and correct.
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This paper presents a pattern recognition method combining kernel-principal component analysis(PCA) and support vector machine(SVM) and its application to electronic nose technology.It can give data reduction and improve the performance of classification by combining the two methods used in complex electronic nose test environments.The experimental results show that the method has higher recognition compared with the simple application of SVM.
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This paper deals with the deployment and evaluation of machine learning classifiers for prediction of tuberculosis. This research paper deploys five key machine learning classifiers Naive Bayes, Support Vector Machine, Decision Tree, K Nearest Neighbors and Random Forest. It is clearly understood that Support Vector Machine provides the best accuracy 99.3 % for the prediction of Pulmonary Tuberculosis (PTB) and Extrapulmonary Tuberculosis (EPTB) when compared with all other machine learning classifiers on Tuberculosis data set. An important challenge in machine learning is to build accurate and competent machine learning classifiers. Hence Support Vector Machine is a best suited Machine Learning Classifier for prediction of the PTB and EPTB.
Relevance vector machine
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In this paper, SVM and PCA are incorporated to classify brain fMRI images. This method well overcomes the difficulty of classifying high-dimensional data. PCA is utilized to extract the most representative features. SVM classifier based on selected features is trained to decode brain states. Experimental results show that the proposed method yields good performance. The correct classification rate of our bi-class recognition problems reaches as high as 97%.
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