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摘要翻译：
提出了一种新的基于机器学习的方法，称为独立分类器网络（InClass nets）技术，用于条件独立混合模型的非参数估计。我们将CIMM的估计作为一个多类分类问题来研究，因为将数据集划分为不同的类别自然会导致混合模型的估计。InClass网络由多个独立的分类器神经网络组成，每个网络处理CIMM的一个变量。通过使用适当的代价函数同时训练单个神经网络来执行CIMM与数据的拟合。NNs逼近任意函数的能力使我们的技术是非参数的。进一步利用神经网络的能力，我们允许模型的条件独立变量单独是高维的，这是我们的技术相对于现有的非机器学习方法的主要优势。我们得到了关于二元CIMM非参数可辨识性的一些新结果，即二元CIMM可辨识的一个必要条件和一个（不同的）充分条件。我们以Python包RainDancesVI的形式提供了InClass nets的公开实现，并用几个实例验证了我们的InClass nets技术。我们的方法在非监督和半监督分类问题中也有应用。
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英文标题：
《InClass Nets: Independent Classifier Networks for Nonparametric
Estimation of Conditional Independence Mixture Models and Unsupervised
Classification》
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作者：
Konstantin T. Matchev, Prasanth Shyamsundar
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最新提交年份：
2020
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分类信息：
一级分类：Statistics 统计学
二级分类：Machine Learning 机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
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一级分类：Computer Science 计算机科学
二级分类：Machine Learning 机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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一级分类：Economics 经济学
二级分类：Econometrics 计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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一级分类：Physics 物理学
二级分类：High Energy Physics - Phenomenology 高能物理-现象学
分类描述：Theoretical particle physics and its interrelation with experiment. Prediction of particle physics observables: models, effective field theories, calculation techniques. Particle physics: analysis of theory through experimental results.
理论粒子物理及其与实验的相互关系。粒子物理可观测物的预测：模型，有效场论，计算技术。粒子物理：通过实验结果分析理论。
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一级分类：Physics 物理学
二级分类：Data Analysis, Statistics and Probability 数据分析、统计与概率
分类描述：Methods, software and hardware for physics data analysis: data processing and storage; measurement methodology; statistical and mathematical aspects such as parametrization and uncertainties.
物理数据分析的方法、软硬件：数据处理与存储；测量方法；统计和数学方面，如参数化和不确定性。
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一级分类：Statistics 统计学
二级分类：Methodology 方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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英文摘要：
We introduce a new machine-learning-based approach, which we call the Independent Classifier networks (InClass nets) technique, for the nonparameteric estimation of conditional independence mixture models (CIMMs). We approach the estimation of a CIMM as a multi-class classification problem, since dividing the dataset into different categories naturally leads to the estimation of the mixture model. InClass nets consist of multiple independent classifier neural networks (NNs), each of which handles one of the variates of the CIMM. Fitting the CIMM to the data is performed by simultaneously training the individual NNs using suitable cost functions. The ability of NNs to approximate arbitrary functions makes our technique nonparametric. Further leveraging the power of NNs, we allow the conditionally independent variates of the model to be individually high-dimensional, which is the main advantage of our technique over existing non-machine-learning-based approaches. We derive some new results on the nonparametric identifiability of bivariate CIMMs, in the form of a necessary and a (different) sufficient condition for a bivariate CIMM to be identifiable. We provide a public implementation of InClass nets as a Python package called RainDancesVI and validate our InClass nets technique with several worked out examples. Our method also has applications in unsupervised and semi-supervised classification problems.
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PDF链接：
https://arxiv.org/pdf/2009.00131
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