This paper presents a method of medicine composition concentration analysis based on least square support vector machines (LS-SVMs) and examines the importance of the hyperparameter choice in improvement of algorithm ...
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The modeling of topological relations between spatial regions is a primary topic in spatial reasoning, geographic information systems (GIS) and spatial databases. In many geographical applications spatial regions do n...
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The modeling of topological relations between spatial regions is a primary topic in spatial reasoning, geographic information systems (GIS) and spatial databases. In many geographical applications spatial regions do not always have homogeneous interiors and sharply defined boundaries, but frequently their interiors and boundaries are fuzzy. Recently, representing fuzzy spatial regions and modeling the topological relations between them plays an increasingly important theory and application role. Based on the characteristics of fuzzy regions in raster data model and the requirement of topological relations analysis in applications, a hierarchical topological relations model is proposed. The model can determine the topological relation between fuzzy raster regions on multiple levels with the values of three predicate. When predicates are evaluated within two values, it can deal with crisp raster regions as a specific case and there are 5 possible cases of topological relations. When predicates are evaluated within three values, there are 27 possible cases. There are 51 possible cases when predicates are evaluated within six values. In practical applications, the model can analyze topological relations of fuzzy raster regions according to the existing facts and the requirement. The model is wieldy in practical applications and achieves satisfactory results.
Concept lattice, the core data structure in formal concept analysis, has been used widely in machine learning, data mining and knowledge discovery, information retrieval, etc. The main difficulty with concept lattice-...
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Concept lattice, the core data structure in formal concept analysis, has been used widely in machine learning, data mining and knowledge discovery, information retrieval, etc. The main difficulty with concept lattice-based system comes from the lattice construction itself. This paper proposes a new algorithm called SSPCG (search space partition based concepts generation) based on the closures search space partition. The algorithm divides the closures search space into several subspaces in accordance with the criterions prescribed ahead, and introduces an efficient scheme to recognize the valid ones, which bounds searching just in these valid subspaces. An intermediate structure is employed to judge the validity of a subspace and compute closures more efficiently. Since the partition of the search space is recursive and the searching in subspaces is independent, a parallel version can be directly reached. The algorithm is experimental evaluated and compared with the famous NextClosure algorithm proposed by Ganter for random generated data, as well as for real application data. The results show that the algorithm performs much better than the later.
A method for simultaneous analysis of the two components of compound paracetamol and diphenhydramine hydrochloride powdered drugs on near-infrared (NIR) spectroscopy is developed by using a Radial Basis Function (RBF)...
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In this paper, we propose a new supervised compound learning algorithm for training our constructed approximated bivariate non-tensor product adaptive pre-wavelet neural network (APWNN). On the one hand, the linear we...
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In this paper, we propose a new supervised compound learning algorithm for training our constructed approximated bivariate non-tensor product adaptive pre-wavelet neural network (APWNN). On the one hand, the linear weights of APWNN are trained by the self-adaptive learning rate method. On the other hand an extended Kalman filter method is used to update the nonlinear parameters such as dilation parameters and translation parameters. Additionally we demonstrate the efficiency of our proposed method through a concrete example of function approximation.
There are lots of data with multidimensional attributes and spatial position information in precision agriculture applications. Based on the high dimensional spatial clustering algorithm, the agriculture breed partiti...
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There are lots of data with multidimensional attributes and spatial position information in precision agriculture applications. Based on the high dimensional spatial clustering algorithm, the agriculture breed partition method is proposed and applied in Chinese national 863 project. The data mining algorithm is performed in this way: cluster the multidimensional attributes first, then cluster the spatial position information using the former attributes clustering results. Experiments show that the two-phases clustering method is prior to the traditional single-phase clustering method. According to the clustering results, one will decide the region belongs to what kind of soil, select the breeds and the field managements.
This paper presents a method of medicine composition concentration analysis based on least square support vector machines (LS-SVMs) and examines the importance of the hyperparameter choice in improvement of algorithm ...
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This paper presents a method of medicine composition concentration analysis based on least square support vector machines (LS-SVMs) and examines the importance of the hyperparameter choice in improvement of algorithm performance. Simulation results show that the proposed method obtains high quality precision in the generalization, compared with multiple linear regression, and that it is an efficient approach to regression estimation.
A method for simultaneous analysis of the two components of compound paracetamol and diphenhydramine hydrochloride powdered drugs on near-infrared (NIR) spectroscopy is developed by using a radial basis function (RBF)...
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A method for simultaneous analysis of the two components of compound paracetamol and diphenhydramine hydrochloride powdered drugs on near-infrared (NIR) spectroscopy is developed by using a radial basis function (RBF) network. Nearest neighbor-clustering algorithm is used as the learning algorithm of RBF network. Comparisons of the results obtained from the RBF models with those from BP models show that it is feasible to use the RBF network in nondestructive quantitative analysis of the components of drugs.
The optimal partition algorithm (OPA) is applied to the training of parameters in the radial basis function (RBF) neural network. The appropriate modification for the OPA is performed according to the characteristics ...
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The optimal partition algorithm (OPA) is applied to the training of parameters in the radial basis function (RBF) neural network. The appropriate modification for the OPA is performed according to the characteristics of the RBF neural network. The approach for determining the centers and widths of the clustering is added in the modified OPA and applied to choose the centers and widths of the neural network. A method for adjusting the structure of the neural network dynamically is presented by using the difference of the objective functions of the clustering. Thus it is realized to select the number of the hidden nodes adaptively. Simulation results of the stock price prediction demonstrate the effectiveness of the proposed approach. Comparisons with traditional algorithms show that the proposed OPA method possesses obvious advantages in the precision of forecasting, generalization, and forecasting trends. Simulations also show that the algorithm combining the OPA with the orthogonal least squares (OLS) possesses more superior performance in the rightness of forecasting trends.
This paper presents a Case Based Reasoning (CBR) approach to identifying micro-architecture anti-patterns and replacing them with "good" patterns in order to improve the design of software system. The result...
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