
Compensation Learning
Weighting strategy prevails in machine learning. For example, a common a...
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A comparison of Deep Learning performances with others machine learning algorithms on credit scoring unbalanced data
Training models on highly unbalanced data is admitted to be a challengin...
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Which Samples Should be Learned First: Easy or Hard?
An effective weighting scheme for training samples is essential for lear...
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Intuitiveness in Active Teaching
Machine learning is a doubleedged sword: it gives rise to astonishing r...
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SapAugment: Learning A Sample Adaptive Policy for Data Augmentation
Data augmentation methods usually apply the same augmentation (or a mix ...
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Improved Preterm Prediction Based on Optimized Synthetic Sampling of EHG Signal
Preterm labor is the leading cause of neonatal morbidity and mortality a...
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Regularization and Optimization strategies in Deep Convolutional Neural Network
Convolution Neural Networks, known as ConvNets exceptionally perform wel...
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A Mathematical Foundation for Robust Machine Learning based on BiasVariance Tradeoff
A common assumption in machine learning is that samples are independently and identically distributed (i.i.d). However, the contributions of different samples are not identical in training. Some samples are difficult to learn and some samples are noisy. The unequal contributions of samples has a considerable effect on training performances. Studies focusing on unequal sample contributions (e.g., easy, hard, noisy) in learning usually refer to these contributions as robust machine learning (RML). Weighing and regularization are two common techniques in RML. Numerous learning algorithms have been proposed but the strategies for dealing with easy/hard/noisy samples differ or even contradict with different learning algorithms. For example, some strategies take the hard samples first, whereas some strategies take easy first. Conducting a clear comparison for existing RML algorithms in dealing with different samples is difficult due to lack of a unified theoretical framework for RML. This study attempts to construct a mathematical foundation for RML based on the biasvariance tradeoff theory. A series of definitions and properties are presented and proved. Several classical learning algorithms are also explained and compared. Improvements of existing methods are obtained based on the comparison. A unified method that combines two classical learning strategies is proposed.
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