In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

[Q]: This paper describes a new method for reducing the error in a classifier. It uses a weight adjustment update, but includes the very simple rule of either adding or subtracting the adjustment, based on whether the data point is currently larger or smaller than the desired value, and on a pointby-point basis. This gives added flexibility to the convergence procedure, where through a series of transpositions, values far away can continue towards the desired value, whereas values that are originally much closer can oscillate from one side to the other. Tests show that the method can successfully classify some known datasets. It can also work in a batch mode, with reduced training times and can be used as part of a neural network, or classifiers in general. There are also some updates on an earlier wave shape paper.
[A]: A New Oscillating-Error Technique for Classifiers

[Q]: Deep Gaussian processes (DGPs) are multi-layer hierarchical generalisations of Gaussian processes (GPs) and are formally equivalent to neural networks with multiple, infinitely wide hidden layers. DGPs are nonparametric probabilistic models and as such are arguably more flexible, have a greater capacity to generalise, and provide better calibrated uncertainty estimates than alternative deep models. This paper develops a new approximate Bayesian learning scheme that enables DGPs to be applied to a range of medium to large scale regression problems for the first time. The new method uses an approximate Expectation Propagation procedure and a novel and efficient extension of the probabilistic backpropagation algorithm for learning. We evaluate the new method for non-linear regression on eleven real-world datasets, showing that it always outperforms GP regression and is almost always better than state-of-the-art deterministic and sampling-based approximate inference methods for Bayesian neural networks. As a by-product, this work provides a comprehensive analysis of six approximate Bayesian methods for training neural networks.
[A]: Deep Gaussian Processes for Regression using Approximate Expectation Propagation

[Q]: <lb>Ordinal peer grading has been proposed as a simple and scalable solution for com-<lb>puting reliable information about student performance in massive open online courses.<lb>The idea is to outsource the grading task to the students themselves as follows. After<lb>the end of an exam, each student is asked to rank in terms of quality a bundle of<lb>exam papers by fellow students. An aggregation rule will then combine the individ-<lb>ual rankings into a global one that contains all students. We define a broad class of<lb>simple aggregation rules and present a theoretical framework for assessing their ef-<lb>fectiveness. When statistical information about the grading behaviour of students is<lb>available, the framework can be used to compute the optimal rule from this class with<lb>respect to a series of performance objectives. For example, a natural rule known as<lb>Borda is proved to be optimal when students grade correctly. In addition, we present<lb>extensive simulations and a field experiment that validate our theory and prove it to<lb>be extremely accurate in predicting the performance of aggregation rules even when<lb>only rough information about grading behaviour is available.
[A]:
How effective can simple ordinal peer grading be?