基于深度编码器网络的大裕度kNN分类

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摘要翻译：
KNN是最常用的分类方法之一，但由于距离度量选择不当或存在大量与类别无关的特征，它往往不能很好地工作。线性特征变换方法被广泛应用于提取类相关信息以改进kNN分类，但在许多应用中非常有限。核函数已经被用来学习强大的非线性特征变换，但是这些方法不能扩展到大的数据集。本文提出了一种基于约束boltzmann机器预训练的深度神经网络的非线性特征映射方法，用于改进kNN在大边界框架中的分类，我们称之为DNet-KNN。DNet-kNN既可用于分类，又可用于有监督的降维。在两个基准手写体数据集上的实验结果表明，DNet-kNN比基于线性映射的大边界kNN和基于Retrrective boltzmann机预训练的深度自动编码器的kNN具有更好的性能。
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英文标题：
《Large-Margin kNN Classification Using a Deep Encoder Network》
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作者：
Martin Renqiang Min, David A. Stanley, Zineng Yuan, Anthony Bonner,
  and Zhaolei Zhang
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最新提交年份：
2009
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分类信息：

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：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。
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英文摘要：
  KNN is one of the most popular classification methods, but it often fails to work well with inappropriate choice of distance metric or due to the presence of numerous class-irrelevant features. Linear feature transformation methods have been widely applied to extract class-relevant information to improve kNN classification, which is very limited in many applications. Kernels have been used to learn powerful non-linear feature transformations, but these methods fail to scale to large datasets. In this paper, we present a scalable non-linear feature mapping method based on a deep neural network pretrained with restricted boltzmann machines for improving kNN classification in a large-margin framework, which we call DNet-kNN. DNet-kNN can be used for both classification and for supervised dimensionality reduction. The experimental results on two benchmark handwritten digit datasets show that DNet-kNN has much better performance than large-margin kNN using a linear mapping and kNN based on a deep autoencoder pretrained with retricted boltzmann machines.
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PDF链接：
https://arxiv.org/pdf/0906.1814

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关键词：knn 编码器 Applications Experimental Intelligence 训练 手写体 特征 用于 feature