标题： 非线性模型：使用狄利克雷过程混合物 显示英文标题

标题： Nonlinear Models Using Dirichlet Process Mixtures

摘要： 我们引入了一种新的非线性分类模型，在该模型中使用Dirichlet过程混合来非参数地建模响应变量y和协变量x的联合分布。我们在混合成分的每个部分内保持y与x之间的关系为线性；如果混合包含多于一个成分，则整体关系变为非线性。我们利用模拟数据比较了这种方法与简单多项式逻辑（MNL）模型、具有二次项的MNL模型以及决策树模型的表现。我们还在蛋白质折叠分类问题上评估了我们的方法，发现我们的模型相较于基于神经网络（NN）和支持向量机（SVM）的先前方法提供了显著改进。蛋白质折叠类别具有层次结构。我们将方法扩展到存在类别层次结构的分类问题上，发现利用关于蛋白质折叠层次结构的先验信息可以提高预测准确性。 显示英文摘要

摘要： We introduce a new nonlinear model for classification, in which we model the joint distribution of response variable, y, and covariates, x, non-parametrically using Dirichlet process mixtures. We keep the relationship between y and x linear within each component of the mixture. The overall relationship becomes nonlinear if the mixture contains more than one component. We use simulated data to compare the performance of this new approach to a simple multinomial logit (MNL) model, an MNL model with quadratic terms, and a decision tree model. We also evaluate our approach on a protein fold classification problem, and find that our model provides substantial improvement over previous methods, which were based on Neural Networks (NN) and Support Vector Machines (SVM). Folding classes of protein have a hierarchical structure. We extend our method to classification problems where a class hierarchy is available. We find that using the prior information regarding the hierarchical structure of protein folds can result in higher predictive accuracy.