Jenssen. Information theoretic clustering using a k-nearest neighbors approach. Pattern Recognition, 47(9):3070-3081, 2014.V. V. Vikjord and R. Jenssen, "Information theoretic clustering using a k-nearest neighbors approach," Pattern Recognition, vol. 47, no. 9, pp. 3070-3081, Sep. 2014....
V. V. Vikjord and R. Jenssen. Information theoretic clustering using a k-nearest neighbors approach. Pattern Recognition, 47(9):3070-3081, 2014.Vikjord, V.V., Jenssen, R.: Information theoretic clustering using a k-nearest neighbors approach. Pattern Recognition 47 (9), 3070–3081 (2014)...
Addressing the urgent need for effective stress-reduction strategies, our approach integrates the strengths of multiple base classifiers, including random forest, multi-layer perceptron (MLP), decision tree, and K-nearest neighbors (KNN). By combining these classifiers into a hybrid ensemble model, ...
The selected subset is evaluated based on three classifiers K-Nearest Neighbors (KNN), Support Vector Machine (SVM) and Artificial Neural Network (ANN). Our proposed model is validated using three real-world credit datasets. The obtained results confirm the efficiency of SMPSO-KNN model to ...
Innovative Computing, Information and Control . 2007... JL Shih,WC Wang - International Conference on Innovative Computing 被引量: 22发表: 2007年 On Finding k Nearest Neighbors in the Plane The nearest neighbor problem arises in several applications such as density estimation, pattern classification...
After mixing the sampled trajectories and data under each stimulus, for every sampled trajectory, false k-nearest neighbors were counted. A histogram of the number of false k-nearest neighbors (Supplementary Fig. S3b) was fitted to a binomial distribution to obtain the false k-nearest neighbor ...
Keywords:k-nearestneighborsmutualinformation;inputvariablesselection;correlationanalysis 1 引 言 多变量时间序列建模与预测已在天气预报、经 济预测、电力负荷预测等方面得到了广泛的应用.传 统的预测方法很少考虑输入变量之间的关系,如果输 入变量选择不当,则有可能产生较差的预测结果,因 此多变量间的相关分析及输入...
Arroyo et al., 2010, Arroyo et al., 2011, and González-Rivera and Arroyo (2012) developed forecasting methods for interval and histogram time series that were adapted from classical algorithms such as smoothing filters and non-parametric k nearest neighbors (k-NN) methods. Another approach ...
a k-nearest neighbors (kNN) graph of numerical datasets; artificial graphs; and disease comorbidity networks. Table 2 Datasets Full size table To enable visual inspection of clustering results, we generated kNN graphs with parameter k = 30 from selected numerical 2D datasets taken from the...
(i.e. Support Vector Machines, Deep Neural Networks, Random Forests, andk-Nearest Neighbors) were adopted in the experimental environment. Preliminary tests were performed in a within-subject experiment, achieving the best classification accuracy of87.1%±7.8%with a Deep Neural Network. As the ...