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The paper presents a new way to apply text similarity computing to the Clinical Decision Support System. It can be applied to all kinds of diseases. Our method includes some traditional algorithms and their improvements, such as TF-IDF algorithm and Cosine Similarity algorithm. Besides, a new approach using TF-IDF algorithm combined eigenvector associated model to determine the case feature weights...
The randomness of the wind velocity causes the fluctuation of wind power. Therefore, It is necessary to forecast the wind power in a certain time. In this paper, the ultra-short term predication of wind power has been carried out based on the Auto-Regressive-and-Moving-Average (ARMA) model. The wind power was predicted by prediction steps of ARMA in section II. According to the corresponding national...
Collaborative filtering is a widely-used technique in online services to enhance the accuracy of a recommender system. This technique, however, comes at the cost of users having to reveal their preferences, which has undesirable privacy implications. We propose a collaborative filtering system where the system does not observe the users' data and is still able to provide useful recommendations. Compared...
As we approach the era of exascale computing, the role of distributions to summarize, analyze and visualize large scale data is becoming more and more important. Since histograms continue to be a popular way of modeling the underlying data distribution, we propose a scalable and distributed framework for computing histograms from scalar and vector data at different levels of detail required by various...
Recently, a deterministic learning theory was proposed for locally-accurate identification of nonlinear systems. In this paper, we investigate the performance of deterministic learning, including the learning speed and learning accuracy. By analyzing the convergence properties of a class of linear time-varying (LTV) systems, explicit relations between the persistency of excitation (PE) condition (especially...
Microarray gene expression data have been used in cancer discovery and prediction characterized by their small samples and large dimensionality. This paper proposes a hybrid method based on improved Ant Colony Optimization (ACO) and Random Forests (RF) for selecting a small set of marker genes from microarray data to produce high accuracy cancer classifier. The method preselects top-ranked features...
Ant colony optimization (ACO) is a kind of bionic swarm intelligence algorithm belongs to artificial intelligence (AI) field and has been successfully applied in resolving complex optimization problems. Support vector machine (SVM) is a new machine learning method with greater generalization performance, and has shown its superiority in classification and regression problems. By combining the advantages...
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