Rejoinder on 'Imprecise probability models for learning multinomial distributions from data. Applications to learning credal networks'

Andres R. Masegosa; Serafin Moral

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Rejoinder on 'Imprecise probability models for learning multinomial distributions from data. Applications to learning credal networks'

机译：关于不精确概率模型的重新结合，用于从数据中学习多项式分布。在学习网络中的应用

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In this paper we answer to the comments provided by Fabio Cozman, Marco Zaffalon, Giorgio Corani, and Didier Dubois on our paper 'Imprecise Probability Models for Learning Multinomial Distributions from Data. Applications to Learning Credal Networks'. The main topics we have considered are: regularity, the learning principle, the trade-off between prior imprecision and learning, strong symmetry, and the properties of ISSDM for learning graphical conditional independence models.

机译：在本文中，我们回答了法比奥·科兹曼（Fabio Cozman），马可·扎法隆（Marco Zaffalon），乔治·科拉尼（Giorgio Corani）和迪迪埃·杜布瓦（Didier Dubois）在我们的论文“从数据中学习多项式分布的不精确概率模型”中提出的意见。学习Credal网络的应用。我们考虑的主要主题是：规律性，学习原理，先验不精确与学习之间的权衡，强对称性以及ISSDM用于学习图形条件独立模型的属性。

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来源
《高分子論文集》 |2014年第7期|1618-1622|共5页
作者
Andres R. Masegosa; Serafin Moral;
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作者单位

Dpto. Ciencias de la Computacion e Inteligencia Artificial, Universidad de Granada, 18071 Granada, Spain;

Dpto. Ciencias de la Computacion e Inteligencia Artificial, Universidad de Granada, 18071 Granada, Spain;

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收录信息美国《科学引文索引》(SCI);
原文格式 PDF
正文语种 eng
中图分类
关键词
Imprecise probability; Learning; Near-ignorance; Imprecise prior models; Credal networks; Credal classification;

机译：不精确的概率;学习;近乎无知;不精确的先前模型;破坏网络;爬行分类;

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专利

1. Imprecise probability models for learning multinomial distributions from data. Applications to learning credal networks [J] . Andres R. Masegosa, Serafin Moral 高分子論文集 . 2014,第7期

机译：用于从数据中学习多项式分布的不精确概率模型。在学习网络中的应用
2. Comments on 'Imprecise probability models for learning multinomial distributions from data. Applications to learning credal networks' by Andres R. Masegosa and Serafin Moral [J] . Marco Zaffalon, Giorgio Corani 高分子論文集 . 2014,第7期

机译：评论“从数据中学习多项式分布的不精确概率模型。 Andres R. Masegosa和Serafin Moral在学习credal网络中的应用
3. Learning imprecise probability models: Conceptual and practical challenges [J] . Fabio Gagliardi Cozman 高分子論文集 . 2014,第7期

机译：学习不精确的概率模型：概念和实践挑战
4. Unsupervised Learning of a Finite Discrete Mixture Model Based on the Multinomial Dirichlet Distribution: Application to Texture Modeling [C] . Nizar Bouguila, Djemel Ziou International Workshop on Pattern Recognition in Information Systems . 2004

机译：基于多项式Dirichlet分布的有限离散混合模型的无监督学习：纹理建模应用
5. Face modeling and biometric anti-spoofing using probability distribution transfer learning. [D] . Rodrigues, Ricardo Nagel. 2011

机译：使用概率分布转移学习的人脸建模和生物特征识别防欺骗。
6. Learning algorithms and probability distributions in feed-forward and feed-back networks [O] . J. J. Hopfield 1987

机译：前馈和反馈网络中的学习算法和概率分布
7. Credal Sets Approximation by Lower Probabilities: Application to Credal Networks [O] . Ro Antonucci A, Fabio Cuzzolin B, Via Cantonale 2013

机译：低概率的信用集逼近：在信用网络中的应用
8. Application of Adaptive Learning Network Modeling to Single-Particle Erosion Test Data. [R] . Craig, J. N., Cook, F. J. 1982

机译：自适应学习网络建模在单粒子侵蚀试验数据中的应用。

Rejoinder on 'Imprecise probability models for learning multinomial distributions from data. Applications to learning credal networks'

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