The output of the study is a performance comparison of various classifiers especially in terms of generalization to unseen datasets. Feature selection experiments yield the best feature set for the detection of computer worms as time to live (TTL), Internet protocol (IP) packet length, value or ...
Generalization Performance refers to a learner's ability to make accurate predictions on new, unseen data based on its training. It characterizes how well a model can generalize to independent test instances beyond the training data. AI generated definition based on: Quantum Machine Learning, 2014 ...
Generalization performance is a central goal in machine learning, with explicit generalization strategies needed when training over-parametrized models, like large neural networks. There is growing interest in using multiple, potentially auxiliary tasks, as one strategy towards this goal. In this work, ...
论文利用corrupted label和shuffled pixel等对比实验，测试了几种不同的regularizationtechniques, 诸如data a...
1.In the machine learning problems,the consistence andgeneralization performanceare the main research points.在机器学习问题中,学习机器的一致性和推广能力是非常重要的研究课题,通常,学习机器的推广能力与VC维和Vγ维有密切的关系,证明了再生核Hilbert空间中一类范围较广的回归损失函数集的Vγ维对于任意的γ>0有...
Performance evaluation was conducted on several types of datasets in UCI machine learning repository [9] and ATR speech database [10]. In order to compare the performance of the proposed method with other learning methods, the EBP based neural networks, ...
(i.e., generalizing). In this work, we provide a comprehensive study of generalization performance in QML after training on a limited numberNof training data points. We show that the generalization error of a quantum machine learning model withTtrainable gates scales at worst as\sqrt{T/N}. ...
On Adam Trained Models and a Parallel Method to Improve the Generalization Performance Adam is a popular stochastic optimizer that uses adaptive estimates of lower-order moments to update weights and requires little hyper-parameter tuning. So... G Cong,L Buratti - Machine Learning in Hpc Environmen...
In the machine learning problems,the consistence andgeneralization performanceare the main research points. 在机器学习问题中,学习机器的一致性和推广能力是非常重要的研究课题,通常,学习机器的推广能力与VC维和Vγ维有密切的关系,证明了再生核Hilbert空间中一类范围较广的回归损失函数集的Vγ维对于任意的γ>0有限...
When your learner outputs a classifier that is 100% accurate on the training data but only 50% accurate on test data, when in fact it could have output one that is 75% accurate on both, it has overfit. Pedro Domingos - A Few Useful Things to Know About Machine Learning, CACM 55(10)...