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大类别集分类与自适应及其在汉字识别中的应用
Alternative TitleLarge Category Classification and Adaptation with Applications to Chinese Handwriting Recognition
张煦尧
Subtype工学博士
Thesis Advisor刘成林
2013-05-31
Degree Grantor中国科学院大学
Place of Conferral中国科学院自动化研究所
Degree Discipline模式识别与智能系统
Keyword大类别集模式分类 手写汉字识别 降维 局部平滑 修正二次判别函数 分类器自适应 风格迁移映射 模式域分类 Large Category Classification Handwritten Chinese Character Recognition Dimensionality Reduction Local Smoothing Mqdf Adaptation Style Transfer Mapping Pattern Field Classification
Abstract模式分类是机器学习和模式识别的核心问题,而特征表示和分类器设计又是模式分类的关键步骤。大量的特征提取方法以及分类器模型被相继提出并在实际问题中得以广泛应用。然而绝大多数的模型都是针对小类别集问题,并且需要满足独立同分布的假设。因而这些模型在实际问题中会有一定的局限性,例如对于汉字识别这样一个典型的大类别集问题,传统的Fisher线性判别分析降维会带来相似字类别混淆的问题,并且不同的书写人具有迥异的书写风格,因而打破了独立同分布的假设。本文从“大类别集”和“非独立同分布”的角度出发,分别从降维、分类器学习、分类器自适应三方面进行了深入的研究,并且在联机及脱机手写汉字识别上取得了优于传统方法的性能。本文的主要贡献如下: (1)基于加权Fisher准则的大类别集降维方法。为了解决传统的Fisher线性判别分析在大类别集问题中的相似类别混淆问题,本文从加权Fisher准则的角度出发,对容易混淆的类别给予更大的权值,从而获得更优的降维子空间。本文充分比较了五种不同的加权函数以及三种加权空间,在此基础上提出一种非参数降维方法并在大类别集手写汉字识别中取得了最优性能。 (2)局部平滑的修正二次判别函数分类器。为了解决修正二次判别函数MQDF对训练数据的过拟合问题,本文提出一种基于局部平滑的修正二次判别函数LSMQDF,对每一个类的协方差矩阵与其邻近的其他类的协方差矩阵进行平滑处理。作为防止过拟合的正则项、同时也是对全局平滑方法的一种推广,LSMQDF取得了明显的泛化性能提升。 (3)基于风格迁移映射的分类器自适应。为了应对非独立同分布问题,本文提出一种基于风格迁移映射的分类器自适应方法。风格迁移映射是一个将“源点集”映射到“目标点集”的过程,其目标函数是一个凸的二次优化问题因而可以解析求解。风格迁移映射可以与不同的分类器结合并用于监督的、非监督的、及半监督的自适应。大类别集手写汉字识别实验表明,风格迁移映射可以取得显著的错误率下降。 (4)基于风格归一化的模式域(Pattern Field)分类。为了充分利用样本之间的风格一致性以提高分类精度,本文提出了一种基于风格归一化的模式域分类方法。通过对传统的贝叶斯决策方法进行扩展得到了一系列新的训练和决策准则。在多姿态人脸识别,多说话者语音识别,多书写人汉字识别上取得了优于传统方法的性能。
Other AbstractFeature extraction and classifier design are the key problems for pattern classification. Numerous feature extraction methods and classification models have been proposed and applied successfully in the past decades. However, most of them are only suitable for small-category problems and are based on the i.i.d. assumption (independently and identically distributed). Therefore, they cannot fulfill the requirements of real applications such as the handwritten Chinese character recognition (HCCR) problem, which is typical of large category set. For HCCR, the traditional Fisher linear discriminant analysis (FDA) cannot overcome the class separation problem, while the large variability of handwriting styles across individuals breaks the i.i.d. assumption and makes HCCR a challenging problem. To deal with “large category” and “non i.i.d.” problems, from the perspectives of dimensionality reduction, classifier design, and classifier adaptation, this thesis proposed four effective methods summarized as follows. 1. Large category dimensionality reduction based on weighted Fisher criteria (WFC). To solve the class separation problem of the traditional FDA model, using a weighting function to emphasize the close class pairs has been proposed to obtain a better reduced subspace. We evaluate different WFC with five weighting functions and three weighting spaces comprehensively, and further, propose a nonparametric WFC method which can achieve the best performance in handwritten Chinese character recognition. 2. Locally smoothed modified quadratic discriminant function (LSMQDF). To deal with the over-fitting problem of modified quadratic discriminant function (MQDF), we propose the LSMQDF which smoothes the covariance matrix of each class with its neighboring classes. As a regularization to avoid over-fitting and also an extension of the global smoothing method, LSMQDF can improve the generalization performance significantly. 3. Classifier adaptation with style transfer mapping (STM). To deal with the non i.i.d. problem, we propose a classifier adaptation model based on STM, which maps a source point set towards a target point set. The objective function of STM is a convex quadratic programming problem and therefore STM has a closed-form solution. STM can be combined with different types of classifiers for supervised, unsupervised, and semi-supervised adaptation. The experiments on a large scale online handwritten Chinese character recognition problem showed ...
shelfnumXWLW1881
Other Identifier201018014628075
Language中文
Document Type学位论文
Identifierhttp://ir.ia.ac.cn/handle/173211/6550
Collection毕业生_博士学位论文
Recommended Citation
GB/T 7714
张煦尧. 大类别集分类与自适应及其在汉字识别中的应用[D]. 中国科学院自动化研究所. 中国科学院大学,2013.
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