Multi-Target Regression (MTR) is a regression problem of single samples with multiple continuous outputs. The existing multi-target regression algorithms learn regression models based on a same feature space, and ignore the specific characteristics of each output target. To solve the problem, a feature construction algorithm for multi-target regression via radial basis function was proposed. Firstly, clustering was applied to each output target with the output of each target as the additional feature, and according to the centers of clusters, the bases of target specific feature space were constructed in the original feature space. Secondly, the radial basis function was utilized to map the original feature space into the target specific feature space, constructing the target specific features, and then a base regression model was built for each target based on these target specific features. Finally, the low-rank learning method was applied to explore and utilize the correlation between the output targets from the latent space formed by the outputs of base regression models. Experiments were conducted on 18 multi-target regression datasets, and the proposed algorithm was compared with some classical regression algorithms, such as Stacked Single-Target (SST), Ensemble of Regressor Chains (ERC) and Multi-layer Multi-target Regression (MMR). The results show that the proposed algorithm outperforms the comparison algorithms on 14 datasets and achieves the best average performance on 18 datasets. It can be seen that the target specific features can improve the prediction accuracy of each output target and improve the overall prediction performance of multi-target regression by combining the low-rank learning to learn and obtain the correlation between the output targets.

With the rapid development of edge intelligence, the development of existing handwritten numeral recognition convolutional network models has become less and less suitable for the requirements of edge deployment and computing power declining, and there are problems such as poor generalization ability of small samples and high network training costs. Drawing on the classic structure of Convolutional Neural Network (CNN), Leaky_ReLU algorithm, dropout algorithm, genetic algorithm and adaptive and mixed pooling ideas, a handwritten numeral recognition model based on LeNet-DL improved convolutional neural network was constructed. The proposed model was compared on large sample MNIST dataset and small sample REAL dataset with LeNet, LeNet+sigmoid, AlexNet and other algorithms. The improved network has the large sample identification accuracy up to 99.34%, with the performance improvement of about 0.83%, and the small sample recognition accuracy up to 78.89%, with the performance improvement of about 8.34%. The experimental results show that compared with traditional CNN, LeNet-DL network has lower training cost, better performance and stronger model generalization ability on large sample and small sample datasets.

Aiming at the problem that the lip feature dimension is too high and sensitive to the scale space, a technique based on the Scale-Invariant Feature Transform (SIFT) algorithm was proposed to carry out the speaker authentication. Firstly, a simple video frame image neat algorithm was proposed to adjust the length of the lip video to the same length, and the representative lip motion pictures were extracted. Then, a new algorithm based on key points of SIFT was proposed to extract the texture and motion features. After the integration of Principal Component Analysis (PCA) algorithm, the typical lip motion features were obtained for authentication. Finally, a simple classification algorithm was presented according to the obtained features. The experimental results show that compared to the common Local Binary Pattern (LBP) feature and the Histogram of Oriental Gradient (HOG) feature, the False Acceptance Rate (FAR) and False Rejection Rate (FRR) of the proposed feature extraction algorithm are better, which proves that the whole speaker lip motion recognition algorithm is effective and can get the ideal results.

The paper makes up SAS’s shortage in web application by combining SAS and Web application server together.At first,the paper introduced The basic principal of realizing SAS Web application using SAS/IntrNet was introduced,and then was expressed the flow of the program besides clarified the configuring method of configuration files.At last it solved three key problems of programming.During the process of analysis,the paper gave an example of this application—-the realization of Data Analysis System for Internet Negotiation.

Based on time-frequency analysis, the theory of estimating a modulation scale representation was discussed, and a new method of features extraction for speech recognition was proposed. Considering specialty of human auditory perception and disturbances, wavelet analysis was used instead of Fourier analysis for modulation frequency transform, and wavelet modulation scales was acquired as speech features for recognition. For further attenuating the effects of disturbances, subband normalization was introduced with the wavelet modulation scales. Experiments for the Chinese syllables recognition show extracting the wavelet modulation scales as the dynamic features outperform the frequency differences both in noise environments and in time misalignment cases.