Exploring the Learning Difficulty of Data Theory and Measure

by   Weiyao Zhu, et al.

As learning difficulty is crucial for machine learning (e.g., difficulty-based weighting learning strategies), previous literature has proposed a number of learning difficulty measures. However, no comprehensive investigation for learning difficulty is available to date, resulting in that nearly all existing measures are heuristically defined without a rigorous theoretical foundation. In addition, there is no formal definition of easy and hard samples even though they are crucial in many studies. This study attempts to conduct a pilot theoretical study for learning difficulty of samples. First, a theoretical definition of learning difficulty is proposed on the basis of the bias-variance trade-off theory on generalization error. Theoretical definitions of easy and hard samples are established on the basis of the proposed definition. A practical measure of learning difficulty is given as well inspired by the formal definition. Second, the properties for learning difficulty-based weighting strategies are explored. Subsequently, several classical weighting methods in machine learning can be well explained on account of explored properties. Third, the proposed measure is evaluated to verify its reasonability and superiority in terms of several main difficulty factors. The comparison in these experiments indicates that the proposed measure significantly outperforms the other measures throughout the experiments.


page 24

page 28


Understanding Difficulty-based Sample Weighting with a Universal Difficulty Measure

Sample weighting is widely used in deep learning. A large number of weig...

A Mathematical Foundation for Robust Machine Learning based on Bias-Variance Trade-off

A common assumption in machine learning is that samples are independentl...

Which Samples Should be Learned First: Easy or Hard?

An effective weighting scheme for training samples is essential for lear...

A New Perspective on Machine Learning: How to do Perfect Supervised Learning

In this work, we introduce the concept of bandlimiting into the theory o...

Combining piano performance dimensions for score difficulty classification

Predicting the difficulty of playing a musical score is essential for st...

Context-Dependent Similarity

Attribute weighting and differential weighting, two major mechanisms for...

Difficulty Translation in Histopathology Images

The unique nature of histopathology images opens the door to domain-spec...

Please sign up or login with your details

Forgot password? Click here to reset