基于粗糙集的不完备系统关联规则挖掘

在不完备信息系统中挖掘关联规则是一个重要课题。利用粗糙集处理不确定、不完全数据的优势,本文重新定义了关联规则的支持度和置信度,做到不处理缺失数据,直接提取带结论的关联规则。实例表明,所获得的规则简洁与缺失值无关。

SVM method for predicting the thickness of sandstone

The Support Vector Machine （SVM） method can be used to set up a nonlinear function prediction model. It is based on the small sample learning theory. The kernel function can be constructed automatically based on the actual sample data by using the SVM method. As a result, the function not only gets a higher fit precision but is also better generalized. The frequency spectrum and seismic waveform are related by Fourier transform, so they are two different forms of the same physical phenomenon. The variety of waveform character reflects stratigraphic differences and frequency spectrum differences reflect the variation of lithology, fluid composition, and formation thickness. It directly predicts sandstone thickness using the seismic waveform. This not only fully utilizes the seismic information but also greatly increases the accuracy of the prediction. Model examples and actual applications show the applicability of this method.

A genetic Gaussian process regression model based on memetic algorithm

Gaussian process(GP)has fewer parameters,simple model and output of probabilistic sense,when compared with the methods such as support vector machines.Selection of the hyper-parameters is critical to the performance of Gaussian process model.However,the common-used algorithm has the disadvantages of difficult determination of iteration steps,over-dependence of optimization effect on initial values,and easily falling into local optimum.To solve this problem,a method combining the Gaussian process with memetic algorithm was proposed.Based on this method,memetic algorithm was used to search the optimal hyper parameters of Gaussian process regression(GPR)model in the training process and form MA-GPR algorithms,and then the model was used to predict and test the results.When used in the marine long-range precision strike system(LPSS)battle effectiveness evaluation,the proposed MA-GPR model significantly improved the prediction accuracy,compared with the conjugate gradient method and the genetic algorithm optimization process.

Sedimentary microfacies of the H8 member in the Su14 3D seismic test area

The distribution of sedimentary microfacies in the eighth member of the Shihezi formation（the H8 member） in the Sul4 3D seismic test area was investigated.A Support Vector Machine（SVM） model was introduced for the first time as a way of predicting sandstone thickness in the study area.The model was constructed by analysis and optimization of measured seismic attributes.The distribution of the sedimentary microfacies in the study area was determined from predicted sandstone thickness and an analysis of sedimentary characteristics of the area.The results indicate that sandstone thickness predictions in the study area using an SVM method are good.The distribution of the sedimentary microfacies in the study area has been depicted at a fine scale.

Protein domain boundary prediction by combining support vector machine and domain guess by size algorithm

Successful prediction of protein domain boundaries provides valuable information not only for the computational structure prediction of muhi-domain proteins but also for the experimental structure determination. A novel method for domain boundary prediction has been presented, which combines the support vector machine with domain guess by size algorithm. Since the evolutional information of multiple domains can be detected by position specific score matrix, the support vector machine method is trained and tested using the values of position specific score matrix generated by PSI-BLAST. The candidate domain boundaries are selected from the output of support vector machine, and are then inputted to domain guess by size algorithm to give the final results of domain boundary, prediction. The experimental results show that the combined method outperforms the individual method of both support vector machine and domain guess by size.

Forecasting the Advertising investment Risk of Sporting Goods Based on Optimized Support Vector Machine

Forecasting The Advertising investment risk of Sporting goods is very important which can provide the decision support for top manager. In this paper, we presented an optimized support vector machine （OSVM） to predict Advertising investment risk of Sporting goods. Experimental results show that the prediction accuracy improved by the proposed method.

A SVM-kNN method for quasar-star classification

We integrate k-Nearest Neighbors(kNN) into Support Vector Machine(SVM) and create a new method called SVM-kNN.SVM-kNN strengthens the generalization ability of SVM and apply kNN to correct some forecast errors of SVM and improve the forecast accuracy.In addition,it can give the prediction probability of any quasar candidate through counting the nearest neighbors of that candidate which is produced by kNN.Applying photometric data of stars and quasars with spectral classification from SDSS DR7 and considering limiting magnitude error is less than 0.1,SVM-kNN and SVM reach much higher performance that all the classification metrics of quasar selection are above 97.0%.Apparently,the performance of SVM-kNN has slighter improvement than that of SVM.Therefore SVM-kNN is such a competitive and promising approach that can be used to construct the targeting catalogue of quasar candidates for large sky surveys.