Generalised Perceptron Learning

The Margitron: A Generalised Perceptron with Margin

We identify the classical Perceptron algorithm with margin as a member of a broader family of large margin classifiers which we collectively call the Margitron. The Margitron, (despite its) sharing the same update rule with the Perceptron, is shown in an incremental setting to converge in a finite number of updates to solutions possessing any desirable fraction of the maximum margin. Experiments comparing the Margitron with decomposition SVMs on tasks involving linear kernels and 2-norm soft margin are also reported.

Generalised Random Forest Space Overview

Assuming a view of the Random Forest as a special case of a nested ensemble of interchangeable modules, we construct a generalisation space allowing one to easily develop novel methods based on this algorithm. We discuss the role and required properties of modules at each level, especially in context of some already proposed RF generalisations.

RL感知机：高维场景下策略学习的泛化动力学

The RL Perceptron: Generalisation Dynamics of Policy Learning in High Dimensions

该研究提出可解的高维强化学习模型，推导其动力学闭式方程与最优学习调度方案，揭示多种学习特性，并通过游戏实验验证速度-精度权衡，推动高维RL理论与实践的衔接。

多分类教师-学生感知机的学习曲线

Learning curves for the multi-class teacher-student perceptron

本文针对多分类教师-学生感知机，推导高维情形下Bayes最优与ERM泛化误差的渐近表达式，发现正则化交叉熵接近最优，而二分类教师存在一阶相变。

Designing generalisation evaluation function through human-machine dialogue

Automated generalisation has known important improvements these last few years. However, an issue that still deserves more study concerns the automatic evaluation of generalised data. Indeed, many automated generalisation systems require the utilisation of an evaluation function to automatically assess generalisation outcomes. In this paper, we propose a new approach dedicated to the design of such a function. This approach allows an imperfectly defined evaluation function to be revised through a man-machine dialogue. The user gives its preferences to the system by comparing generalisation out…

Unsupervised One-shot Learning of Both Specific Instances and Generalised Classes with a Hippocampal Architecture

Established experimental procedures for one-shot machine learning do not test the ability to learn or remember specific instances of classes, a key feature of animal intelligence. Distinguishing specific instances is necessary for many real-world tasks, such as remembering which cup belongs to you. Generalisation within classes conflicts with the ability to separate instances of classes, making it difficult to achieve both capabilities within a single architecture. We propose an extension to the standard Omniglot classification-generalisation framework that additionally tests the ability to di…