Narrative
发表于 2025-3-23 13:07:07
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Kidney-Failure
发表于 2025-3-23 14:44:08
0302-9743 International Conference on Neural Information Processing, ICONIP 2016, held in Kyoto, Japan, in October 2016. The 296 full papers presented were carefully reviewed and selected from 431 submissions. The 4 volumes are organized in topical sections on deep and reinforcement learning; big data analysi
tariff
发表于 2025-3-23 20:55:21
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invert
发表于 2025-3-24 00:12:21
Hidden Space Neighbourhood Component Analysis for Cancer Classificatione number of samples is equal to the number of features in the feature space. Thus, HSNCA can avoid the small size sample problem. Experimental results on DNA array datasets show that HSNCA is feasibility and efficiency.
Deference
发表于 2025-3-24 04:22:37
Prediction of Bank Telemarketing with Co-training of Mixture-of-Experts and MLPand unlabeled data to construct better prediction model. We performed experiments using real-world data from Portuguese bank. Experiments show that GLCT performs well regardless of the ratio of initial labeled data. Through the series of iterating experiments, we obtained better results on various aspects.
aphasia
发表于 2025-3-24 09:09:31
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产生
发表于 2025-3-24 13:21:07
Neural Network Based Association Rule Mining from Uncertain Data input vectors and visualize the relationship between the items in a database. Distance map based on the weights of winning neurons and support count of items is used as a criteria to prune data space. As shown in our experiments, the proposed SOM is a promising alternative to typical mining algorithms for ARM from uncertain data.
AMBI
发表于 2025-3-24 15:34:18
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intertwine
发表于 2025-3-24 21:34:48
Classifying Human Activities with Temporal Extension of Random Forest the temporal information from the sensors, a modified version of random forest is proposed to preserve the temporal information, and harness them in classifying the human activities. The proposed algorithm is tested on 7 public HAR datasets. Promising results are reported, with an average classific
充满装饰
发表于 2025-3-25 01:05:36
Echo State Network Ensemble for Human Motion Data Temporal Phasing: A Case Study on Tennis Forehandsporal phasing of human motion from captured tennis activity (3D data, 66 time-series). Compared to the optimised Echo State Network (ESN) model achieving 85 % classification accuracy, the ESN ensemble system demonstrates improved classification of 95 % and 100 % accurate phasing state transitions fo