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In this paper we have proposed a new way to achieve the optimum learning rate that can reduce the learning time of the multi layer feed forward neural network. The effect of optimum numbers of inner iterations and numbers of hidden nodes on learning time and recognition rate has been shown. The Principal Component Analysis and Multilayer Feed Forward Neural Network are applied in face recognition...
We present a new algorithm for learning a convex set in n-dimensional space given labeled examples drawn from any Gaussian distribution. The complexity of the algorithm is bounded by a fixed polynomial in n times a function of k and ϵ where k is the dimension of the normal subspace (the span of normal vectors to supporting hyperplanes of the convex set) and the output is a hypothesis that correctly...
This research aims at developing an optimal neural network based DSS, which is aimed at precise and reliable diagnosis of chronic active hepatitis (CAH) and cirrhosis (CRH). The principal component analysis neural network is designed scrupulously for classification of these diseases. The neural network is trained by eight quantified texture features, which were extracted from five different region...
Principal component analysis (PCA) and Fisher discriminate analysis (FDA) of holistic approach of Information theory have been analyzed. Two steps for recognition are taken: training and testing. In the training phase a set of the eigenvectors of the covariance matrix of the images used for training. These eigenvectors are also called as eigenfaces. In testing phase when a new input image is given...
In this paper we propose fast face recognition system based on the Kekre's transform. This algorithm can be easily implemented and number of coefficients required for recognition reduces drastically compared to benchmark algorithm PCA. Thus computational burden decreases. We have compared the performance of this transform with conventional transforms like DCT, DST, Slant transform and WHT. The algorithm...
This paper proposes a frontal staircase detection algorithm using both classical Haar-like features and a novel set of PCA-base Haar-like features. Real AdaBoost is used for training a cascaded classifier. The PCA-based Haar-like features are extremely efficient at rejecting background regions at early stages in the cascade. A specifically designed scanning scheme made the algorithm constantly time...
Statistical timing analysis for manufacturing variability requires modeling of spatially-correlated variation. Common grid-based modeling for spatially-correlated variability involves a trade-off between accuracy and computational cost, especially for PCA (principal component analysis). This paper proposes to spatially interpolate variation coefficients for improving accuracy instead of fining spatial...
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