发帖
雷达卡
摘要翻译：
本文对多类LogitBoost进行分类时的模型学习进行了改进。基于统计学的观点，LogitBoost可以被看作是加法树回归。该设置中的两个重要因素是：1）由于和至零约束导致的耦合分类器输出；2）计算树节点分裂增益和节点值拟合时产生的密集Hessian矩阵。一般来说，对于一个易于处理的模型学习算法来说，这种设置太复杂了。但是，过于简化设置可能会导致性能下降。例如，原始的LogitBoost被ABC-LogitBoost超越，这是因为后者对上述两个因素处理得更仔细。在本文中，我们提出了一些技术来解决LogitBoost设置的两个主要困难：1）我们采用向量树（即。2）在计算树分裂增益和节点值时，我们使用了一种自适应块坐标下降法，利用了密集黑森算法。与原始的和ABC-LogitBoost实现相比，对于一系列公共数据集，我们观察到了更高的分类精度和更快的收敛速度。
---
英文标题：
《AOSO-LogitBoost: Adaptive One-Vs-One LogitBoost for Multi-Class Problem》
---
作者：
Peng Sun, Mark D. Reid, Jie Zhou
---
最新提交年份：
2012
---
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
--
一级分类：Computer Science 计算机科学
二级分类：Artificial Intelligence 人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
--
一级分类：Computer Science 计算机科学
二级分类：Computer Vision and Pattern Recognition 计算机视觉与模式识别
分类描述：Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.
涵盖图像处理、计算机视觉、模式识别和场景理解。大致包括ACM课程I.2.10、I.4和I.5中的材料。
--
---
英文摘要：
This paper presents an improvement to model learning when using multi-class LogitBoost for classification. Motivated by the statistical view, LogitBoost can be seen as additive tree regression. Two important factors in this setting are: 1) coupled classifier output due to a sum-to-zero constraint, and 2) the dense Hessian matrices that arise when computing tree node split gain and node value fittings. In general, this setting is too complicated for a tractable model learning algorithm. However, too aggressive simplification of the setting may lead to degraded performance. For example, the original LogitBoost is outperformed by ABC-LogitBoost due to the latter's more careful treatment of the above two factors. In this paper we propose techniques to address the two main difficulties of the LogitBoost setting: 1) we adopt a vector tree (i.e. each node value is vector) that enforces a sum-to-zero constraint, and 2) we use an adaptive block coordinate descent that exploits the dense Hessian when computing tree split gain and node values. Higher classification accuracy and faster convergence rates are observed for a range of public data sets when compared to both the original and the ABC-LogitBoost implementations.
---
PDF链接：
https://arxiv.org/pdf/1110.3907
京公网安备 11010802022788号
