危险
发表于 2025-3-28 16:07:02
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扔掉掐死你
发表于 2025-3-28 20:09:50
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Concrete
发表于 2025-3-28 22:57:15
Feature Reduction Using Standard Deviation with Different Subsets Selection in Sentiment Analysisexperiment of performance estimation on sentiment analysis dataset using ensemble of classifiers when dimensionality reduction is performed on the input space using three different methods. Also different types of base classifiers and classifier combination rules were used.
Resign
发表于 2025-3-29 03:56:17
Using Multi-agent Systems Simulations for Stock Market Predictions evolving by using genetic programming in order to obtain new agents with better trading strategies generated from combining the trading strategies of the best performing agents and thus replacing the agents which have the worst performing trading strategies.
木质
发表于 2025-3-29 09:19:24
The Influence of a Classifiers’ Diversity on the Quality of Weighted Aging Ensemblerried out on two benchmark databases. The main objective of the experiments was to answer the question if the chosen modified criterion based on the diversity measure and accuracy is an appropriate choice to prune the classifier ensemble dedicated to data stream classification task.
浮雕
发表于 2025-3-29 14:58:06
Bootstrapping and Rule-Based Model for Recognizing Vietnamese Named EntityEs..Our experimented corpus is generated from about 250.034 online news articles and over 9.000 literatures. Our VNER system consists 27 categories and more 300.000 VNEs which are recognized and categorized. The accuracy of the recognizing and classifying algorithm is about 95%.
障碍物
发表于 2025-3-29 16:24:09
Customer Lifetime Value and Defection Possibility Prediction Model Using Machine Learning: An Applicat both the C4.5 and SVM classifications perform well, and by obtaining frequency distributions of the defection possibility, we can predict the number of customers defecting and the number of customers retained.
伤心
发表于 2025-3-29 19:55:30
Comparison of Ensemble Approaches: Mixture of Experts and AdaBoost for a Regression Problemy including nonparametric tests followed by post-hoc procedures designed especially for multiple . comparisons. No statistically significant differences were observed among the best ensembles: two generated by mixture of experts and two by AdaBoost.R2 employing multilayer perceptrons and general linear models as base learning algorithms.
直觉没有
发表于 2025-3-30 02:03:00
The AdaBoost Algorithm with the Imprecision Determine the Weights of the Observations of experiments have been carried out on eight data sets available in the UCI repository and on two randomly generated data sets. The obtained results are compared with the original AdaBoost algorithm using appropriate statistical tests.
认识
发表于 2025-3-30 05:26:39
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