Multiple birth support vector machine based on recurrent neural networks

Graph Regularized Structured Output SVM for Early Expression Detection With Online Extension

In this study, a graph regularized algorithm for early expression detection (EED), called GraphEED, is proposed. EED is aimed at detecting the specified expression in the early stage of a video. Existing EED detectors fail to explicitly exploit the local geometrical structure of the data distribution, which may affect the prediction performance significantly. According to manifold learning, the data in real-world applications are likely to reside on a low-dimensional submanifold embedded in the high-dimensional ambient space. The proposed graph Laplacian consists of two parts: 1) a k -nearest neighbor graph is first constructed to encode the geometrical information under the manifold assumption and 2) the entire expressions are regarded as the must-link constraints since they all contain the complete duration information and it is shown that this can also be formulated as a graph regularization. GraphEED is to have a detection function representing these graph structures. Even with the inclusion of the graph Laplacian, the proposed GraphEED has the same computational complexity as that of the max-margin EED, which is a well-known learning-based EED, but the detection performance has been largely improved. To further make the model appropriate in large-scale applications, with the technique of online learning, the proposed GraphEED is extended to the so-called online GraphEED (OGraphEED). In OGraphEED, the buffering technique is employed to make the optimization practical by reducing the computation and storage cost. Extensive experiments on three video-based datasets have demonstrated the superiority of the proposed methods in terms of both effectiveness and efficiency.

The Prediction of Carbon Emission Information in Yangtze River Economic Zone by Deep Learning

This study aimed to respond to the national “carbon peak” mid-and long-term policy plan, comprehensively promote energy conservation and emission reduction, and accurately manage and predict carbon emissions. Firstly, the proposed method analyzes the Yangtze River Economic Belt as well as its “carbon peak” and carbon emissions. Secondly, a support vector regression (SVR) machine prediction model is proposed for the carbon emission information prediction of the Yangtze River Economic Zone. This experiment uses a long short-term memory neural network (LSTM) to train the model and realize the experiment’s prediction of carbon emissions. Finally, this study obtained the fitting results of the prediction model and the training model, as well as the prediction results of the prediction model. Information indicators such as the scale of industry investment, labor efficiency output, and carbon emission intensity that affect carbon emissions in the “Yangtze River Economic Belt” basin can be used to accurately predict the carbon emissions information under this model. Therefore, the experiment shows that the SVR model for solving complex nonlinear problems can achieve a relatively excellent prediction effect under the training of LSTM. The deep learning model adopted herein realized the accurate prediction of carbon emission information in the Yangtze River Economic Zone and expanded the application space of deep learning. It provides a reference for the model in related fields of carbon emission information prediction, which has certain reference significance.