Urban Real Estate Market Early Warning Based on Support Vector Machine: A Case Study of Beijing

Wang, Xianjia; Zeng, Guan-Tian; Zhang, Kexin; Chen, Haibo; Chen, Zhen‐Song

doi:10.2991/ijcis.d.200129.001

Cited by 10 publications

(8 citation statements)

References 35 publications

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“…e recommendation effect in the case of special value is not as good as the recommendation effect of the combination of the two. Verify the effectiveness of formula (15). e results show that no matter how many keywords there are, the model recommends the best effect.…”

Section: Experimental Design and Analysismentioning

confidence: 91%

“…e model can present the blueprint of facts, abstract representation of facts, and look for similar facts, so as to avoid the threat to society and economy caused by wrong information (Englmeier) [14]. Wang et al built an urban real estate early warning model based on multi-classification support vector mechanism, analyzed the real estate market from all aspects by selecting early warning index data, and the results successfully predicted the market operation of the next year, providing an accurate and reliable method for market early warning (Wang et al) [15]. In order to prevent the collapse of the stock market, Habibi uses the autoregressive model to establish the early warning system and uses the recursive formula of the least squares estimation of the regression parameters to obtain the EWS index probability.…”

Section: Related Workmentioning

confidence: 99%

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Early Warning of Real Estate Market Development Risk Based on Network News Topic Mining and Neural Network

Shen

2022

Mathematical Problems in Engineering

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With the rapid development of the information technology, mining news content efficiently is a difficult problem faced by the government and enterprises. The classification, clustering, and prediction of neural network are used in news mining. A keyword based on neural network and word vector are proposed as a text feature model, and the model is compared with other neural network models. This paper studies the vectorization of text for similarity recommendation and introduces two models: word vector model and text vector model doc2vec based on neural network. In the model with word vector as feature vector, the recommendation accuracy is about 75.35%, doc2vec model is about 44.5%, and the recommendation accuracy of the model with keyword as text is about 88.61%. The recommendation accuracy is about 91%, and the performance has greatly improved. The more the number of keywords, the better the recommendation effect of the model. When the number is about 20, the improvement of the recommendation effect tends to be gentle, and the continuous increase of the number of keywords will increase the operation time of the model. It is proposed that the keyword and word vector based on neural network together as the text vector model can more accurately mine news data, quickly obtain news information, and make prediction and early warning for many industries such as the real estate industry.

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Section: Experimental Design and Analysismentioning

confidence: 91%

Section: Related Workmentioning

confidence: 99%

Early Warning of Real Estate Market Development Risk Based on Network News Topic Mining and Neural Network

Shen

2022

Mathematical Problems in Engineering

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“…However, there are two disadvantages in the practical application of Bayesian classifier: first, if the probability density function of the early warning does not conform to the Gaussian distribution, it will not get good classification results; second, the classifier trained with a small number of training sample sets will produce errors when classifying a large number of sample spaces [11]. Wang et al proposed the economic early warning based on Bayesian decision classification [4]. is early warning system based on probabilistic pattern classification has opened a new research field for economic early warning methods.…”

Section: Related Workmentioning

confidence: 99%

“…In the agricultural development of information technology, due to the popularity of the Internet, the price information of agricultural products is transparent development, agricultural products through the adaptation to the market environment. Support vector machine (SVM) is a machine learning algorithm developed based on statistical learning theory and structural risk minimization theory [4]. It uses support vector to determine the optimal partition hyperplane to segment data space and was first used for classification and recognition of complex information.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Informatization Early Warning Analysis of Agricultural Economy Based on Support Vector Sequential Regression Model

Yang

2021

Journal of Mathematics

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The development of science and technology has laid a solid foundation for the economic informatization of agriculture, and at the same time it brought technical guarantee for the development of agriculture, and the development of agriculture has provided an important material foundation for the development of science and technology. How to identify and deeply study agricultural economic informatization, give early warning to risk information, and ensure the steady development of the whole industry has become a key issue in the application of Internet technology in the field of agricultural development. This paper studies the present situation of agricultural economy informatization development process and applies support vector machine to forecast regional economic development level. The warning limit of agricultural economic growth rate is obtained on the basis of warning situation and warning indicator in early warning index system. The economic early warning model is established based on the support vector sequential regression method, and then the data is trained by MATLAB software to verify the rationality of the early warning model, and the accuracy and corresponding error of the model are given. Experimental results show that the prediction accuracy is 99.3%, the error is less than 0.05, and the prediction effect is relatively ideal, for agricultural economic intelligence information to provide accurate warning and agricultural economic research agricultural commercial development to provide support.

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“…Recently, multitag learning has also been applied to more scenarios such as gene classification, automatic video annotation, and music classification. Each gene may have different kinds of markers for its function, such as metabolism and protein synthesis; in automatic video annotation, the content of each video may be associated with multiple things, such as pedestrians and buildings [17]. ere are two main approaches to multitag learning: one is problem transformation and the other is algorithmic adaptation.…”

Section: Rbf Algorithm For Constructive Englishmentioning

confidence: 99%

A Practical Exploration of Constructive English Learning Platform Informatization Based on RBF Algorithm

Tang

Tsai

2021

Mathematical Problems in Engineering

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The constructive English learning approach provides better technical support and learning concepts for English learners. This paper firstly introduces the theoretical basis of constructive English learning and the status of domestic and international research and analyzes the advantages over the traditional English learning approach. This paper also analyzes the RBF neural network model, especially the locally adjusted RBF algorithm, and considers the training model and construction method of the RBF neural network to simulate the learning pattern of good learners of English in grades 4 and 6 to better realize the effect of the English learning platform and allow the English learning platform to play a supportive role in the learning process of students, and through the evaluation of the design, we can better improve the software design, which can be improved by evaluating the design. In the subsequent research, we should add a more user-friendly interface design, use larger model samples to train the model to make it more universal in accuracy and ensure good stability in operation, and integrate multiple algorithms to improve the training algorithm of RBF neural network to obtain a more general algorithm model. The rich teaching methods can cultivate students’ independent inquiry ability, and the construction of this English learning platform can help improve students’ ability to use information technology. By making students become the main subject of teaching and learning, we can stimulate students’ enthusiasm and a good awareness of English learning. Using the radial basis function (RBF) algorithm and the constructive pod language teaching method, this English learning platform is a good and positive aid for students to learn English, and as a better English education tool, it has certain research value and positive significance for the study of English teaching.

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Urban Real Estate Market Early Warning Based on Support Vector Machine: A Case Study of Beijing

Cited by 10 publications

References 35 publications

Early Warning of Real Estate Market Development Risk Based on Network News Topic Mining and Neural Network

Early Warning of Real Estate Market Development Risk Based on Network News Topic Mining and Neural Network

Intelligent Informatization Early Warning Analysis of Agricultural Economy Based on Support Vector Sequential Regression Model

A Practical Exploration of Constructive English Learning Platform Informatization Based on RBF Algorithm

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