Fang,D.-Z. Guo.Uncertainty and Its Propagation in Spatial Data Mining.Shuju Caiji Yu Chuli/Journal of Data Acquisition and Processing, vol. 19, pp. 475-480. 2004He Bin bin 1,2 , Fang Tao 2, Guo Da zhi 1 ,.Uncertainty and Its Propagation in Spatial Data Mining.[J];Journal of ...
Book2022, Predictive Modeling in Biomedical Data Mining and Analysis Aman Kataria, ... Meetali Chauhan Explore book 3 Methodology The dataset used in this work consists of parameters of Fibrinogen and Globulin of one person, which was recorded for 125 days. The Back Propagation neural network is...
Backpropagation is designed to test for errors working back from output nodes to input nodes. It's an important mathematical tool for improving the accuracy of predictions indata miningand machine learning (ML) processes. Essentially, backpropagation is an algorithm used to quickly calculate derivativ...
Mining important conceptual patterns is an essential task for understanding the context and content of complex data in many scientific and engineering applications. While exact relevance indices in Formal Concept Analysis provide accurate importance evaluation that can be used for extracting interesting conce...
"A Novel PSO Based Back Propagation Learning-MLP (PSO-BP-MLP) for Classification," Computational Intelligence in Data Mining-Volume 2, Smart Innovation, Systems and Technologies, vol.32, pp.461-471, 2015D. Himansu, N. Ajay, N. Bighnaraj, H. S. Behera, "A Novel PSO Based Back ...
Courses on data mining or machine learning will usually start withclustering, because it is both simple and useful. It is an important part of a somewhat wider area of Unsupervised Learning, where the data we want to describe is not labeled. In most cases this is where the user did not ...
In the era of single-cell sequencing, there is a growing need to extract insights from data with clustering methods. Here, we introduce Forest Fire Clustering, an efficient and interpretable method for cell-type discovery from single-cell data. Forest Fire Clustering makes minimal prior assumptions...
Heskes, T. (2016). Expectation Propagation. In: Sammut, C., Webb, G. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7502-7_95-1 Download citation .RIS .ENW
In addition, the model simulates and compares the rumor and counter-rumor propagation process and the influence of parameter changes by using simulated data and real networks, which provides a theoretical basis for preventing and reducing rumor propagation. Section Quantum superposition theory introduces...
in Proceedings of the 2010 SIAM International Conference on Data Mining, 583-594. doi:10.1137/1.9781611972801.51 Usage General See example_minprop.py for the usage of the MINProp-related functions. Example Protein-disease association prediction using 2 homo subnetworks: the protein-protein ...