Soft Margin Bayes-Point-Machine Classification via Adaptive Direction Sampling
Soft Margin Bayes-Point-Machine Classification via Adaptive Direction Sampling
复制标题
通过自适应方向采样进行软边距贝叶斯点机器分类
DOI：
10.1007/978-3-319-59126-1_26
复制
发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Jörn Ostermann
中科院分区：
文献类型：
--
作者：
Karsten Vogt;Jörn Ostermann
监督机器学习是许多涉及数据处理和决策的应用程序的重要构建模块。好的分类器经过训练可以在训练集上产生准确的预测，同时也可以很好地推广到未见过的数据。为此，过去提出了贝叶斯点机器（bpm）作为边缘最大化分类器的泛化，例如支持向量机（svm）。对于bpms，最佳分类器被定义为对适当选择的后验分布的期望，可以通过马尔可夫链蒙特卡罗（mcmc）采样来估计。在本文中，我们对原始 bpm 分类器提出了三项改进。我们的新统计模型根据样本大小进行正则化，并允许真正的软边际公式，而无需手动调整任何令人讨厌的参数。其次，该模型可以原生处理多类问题。最后，我们的快速自适应mcmc采样器使用自适应方向采样（ads），并且可以从建议的后验生成样本，其运行时复杂度与训练集大小成二次方。因此，我们将新的分类器称为多类软边缘贝叶斯点机 (ms-bpm)。我们在多个数据集上评估了我们的方法的泛化能力，并表明我们的软边缘模型在原始 bpm 的基础上显着改进，特别是对于小型训练集，并且与 svm 分类器具有竞争力。我们还表明，从我们的模型生成的类成员概率改进了 Platt 缩放，这是一种从最大边缘分类器导出校准概率的流行方法。
Supervised machine learning is an important building block for many applications that involve data processing and decision making. Good classifiers are trained to produce accurate predictions on a training set while also generalizing well to unseen data. To this end, Bayes-Point-Machines (bpm) were proposed in the past as a generalization of margin maximizing classifiers, such as Support-Vector-Machines (svm). Forbpms, the optimal classifier is defined as an expectation over an appropriately chosen posterior distribution, which can be estimated via Markov-Chain-Monte-Carlo (mcmc) sampling. In this paper, we propose three improvements on the originalbpmclassifier. Our new statistical model is regularized based on the sample size and allows for a true soft-margin formulation without the need to hand-tune any nuisance parameters. Secondly, this model can handle multi-class problems natively. Finally, our fast adaptivemcmcsampler uses Adaptive Direction Sampling (ads) and can generate a sample from the proposed posterior with a runtime complexity quadratic in the size of the training set. Therefore, we call our new classifier the Multi-class-Soft-margin-Bayes-Point-Machine (ms-bpm). We have evaluated the generalization capabilities of our approach on several datasets and show that our soft-margin model significantly improves on the originalbpm, especially for small training sets, and is competitive withsvmclassifiers. We also show that class membership probabilities generated from our model improve on Platt-scaling, a popular method to derive calibrated probabilities from maximum-margin classifiers.
在版本空间中打台球
影响因子：
2.9
作者：
P. Rujan
通讯作者：
P. Rujan