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Predicting the life cycle and the short-term popularity of a Web object is important for network architecture optimization. In this paper, we attempt to predict the popularity of a Web object given its historical access records using a novel neural network technique, reservoir computing (RC). The traces of popular videos at YouTube for five continuous months are taken as a case study. We compare RC...
This paper presents an intra-modal fusion environment to integrate multiple raw palm images at low level. Fusion of palmprint instances is performed by wavelet transform and decomposition. To capture the palm characteristics, fused image is convolved with Gabor wavelet transform. The Gabor wavelet feature representation reflects very high dimensional space. To reduce the high dimensionality, ant colony...
Convertible bonds (CB) contain many kinds of embedded options and the complexity of their interaction makes hedging exposures of CBs challengeable. In order to tackle the issue, this paper introduced support vector machine (SVM) approach to overcome the shortcomings of traditional pricing methods and enhance hedging efficiency. By feature selection, kernel function determination and parameter optimization,...
Real-world design optimization problems are typically computationally-expensive and to address this various model-assisted evolutionary frameworks have been proposed. However, often such problems are also high-dimensional and in such settings models tend to have poor accuracy and thus degrade the optimization search. To address this we propose two complementary dimensionality-reduction frameworks...
Computational complexity is one of the most important issues in any machine-learning algorithm. A novel working set selection mechanism is proposed to improve Support Vector Machine (SVM) learning. Implementation is based on the Keerthi et al.'s SMO algorithm, but our approach is one-class classification. When selecting samples for the optimization process, much effort is spent to find the most violating...
This paper presents a motion-adaptive temporal filtering scheme based on trained least-square filtering. A novel iterative method is utilized in the training stage to find the optimal motion-adaptive temporal filter coefficient. The proposed approach shows optimal noise reduction performance in the LMS sense without introducing any blur.
Metamodels based on responses from designed (numerical) experiments may form efficient approximations to functions in engineering analysis. They can improve the efficiency of engineering optimization substantially by uncoupling computationally expensive analysis models and (iterative) optimization procedures. This paper investigated the kriging metamodel approach. In order to prove accuracy and efficiency...
The control for turning process was a complicated problem and the suitable turning parameters are instrumental to turning process, the turning parameters optimized with artificial neural networks was proposed in this paper. Artificial neural networks was a non-linear system with strong non-linear modeling ability, but the traditional BP neural networks has many shortcomings like easily step into local...
This paper presents the evolutionary neural network (ENN) model for the prediction of output from a grid-connected photovoltaic system installed at Malaysian Energy Centre (PTM), Bangi, Malaysia. The ENN model had been developed using evolutionary programming (EP) through the optimization of the number of nodes in the hidden layer, the learning rate and the momentum rate. The ENN model employs solar...
The spectral histogram features are not invariant to images' scale transformation. We investigate in the technique of scale-invariant feature extraction. An approach is proposed to get the characteristic scales based on the reliable keypoints which are detected as local extrema in combination of normalized derivatives. Making use of characteristic scale of image content, which reflects characteristic...
This paper presents a method for recovering 3D facial shape from single image via learning the relationship between the 2D intensity images and the 3D facial shapes. With a coupled training set, the intensity images and their corresponding facial shapes make up two vector spaces respectively. But only the correlated components in both spaces are useful for inference, so there must be embedded hidden...
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