Add to ORKGPlotting a learner's generalization performance against the training set size results in a so-called learning curve. This tool, providing insight in the behavior of the learner, is also practically valuable for model selection, predicting the effect of more training data, and reducing the computational complexity of training. We set out to make the (ideal) learning curve concept precise and briefly discuss the aforementioned usages of such curves. The larger part of this survey's focus, however, is on learning curves that show that more data does not necessarily leads to better generalization performance. A result that seems surprising to many researchers in the field of artificial intelligence. We point out the significance of these findin...
Three factors enter into analyses of performance curves such as learning curves: the amount of train...
In behavioural testing, system functionalities underrepresented in the standard evaluation setting (...
AbstractWe consider the standard problem of learning a concept from random examples. Here alearning ...
The learning curve illustrates how the generalization performance of the learner evolves with more t...
Abstract (98 words) Most tasks get faster with practice. This holds across task size and task type. ...
Data from student learning provide learning curves that, ideally, demonstrate improvement in student...
One of the fundamental machine learning tasks is that of predictive classification. Given that organ...
Estimation of learning curves is ubiquitously based on proportions of correct responses within movin...
Learning curves show how a neural network is improved as the number of training examples increases a...
A learning curve displays the measure of accuracy/error on test data of a machine learning algorithm...
Estimation of learning curves is ubiquitously based on proportions of correct responses within movin...
Gradient-based deep-learning algorithms exhibit remarkable performance in practice, but it is not we...
A machine learning (ML) system must learn not only to match the output of a target function on a tra...
<p>(A): Learning speed when , or , and . The bar graphs and error bars depict sample means and stand...
In their thought-provoking paper [1], Belkin et al. illustrate and discuss the shape of risk curves ...
Three factors enter into analyses of performance curves such as learning curves: the amount of train...
In behavioural testing, system functionalities underrepresented in the standard evaluation setting (...
AbstractWe consider the standard problem of learning a concept from random examples. Here alearning ...
The learning curve illustrates how the generalization performance of the learner evolves with more t...
Abstract (98 words) Most tasks get faster with practice. This holds across task size and task type. ...
Data from student learning provide learning curves that, ideally, demonstrate improvement in student...
One of the fundamental machine learning tasks is that of predictive classification. Given that organ...
Estimation of learning curves is ubiquitously based on proportions of correct responses within movin...
Learning curves show how a neural network is improved as the number of training examples increases a...
A learning curve displays the measure of accuracy/error on test data of a machine learning algorithm...
Estimation of learning curves is ubiquitously based on proportions of correct responses within movin...
Gradient-based deep-learning algorithms exhibit remarkable performance in practice, but it is not we...
A machine learning (ML) system must learn not only to match the output of a target function on a tra...
<p>(A): Learning speed when , or , and . The bar graphs and error bars depict sample means and stand...
In their thought-provoking paper [1], Belkin et al. illustrate and discuss the shape of risk curves ...
Three factors enter into analyses of performance curves such as learning curves: the amount of train...
In behavioural testing, system functionalities underrepresented in the standard evaluation setting (...
AbstractWe consider the standard problem of learning a concept from random examples. Here alearning ...