基于双阈值继承型AdaBoost算法的人脸快速检测

提出增强型Haar-Like特征和基于双阈值的弱分类器快速训练方法,然后在此基础上提出了继承型AdaBoost算法,后层分类器的训练是建立在前层分类器基础之上.该方法具有很快的训练速度,弥补了AdaBoost算法训练速度慢,使得弱分类器数目大大减少,还使得总体检测器的性能得以加强.在实验过程中,首先使用MIT-CBCL库在同样的训练样本和测试样本条件下对几种方法进行了实验,结果表明该方法在训练速度、测试精度及检测时间等方面都优于相应的方法,最后在MIT CMU人脸测试库进一步验证算法的有效性.     

一种基于Adaboost-SVM的高层次语义概念提取方法

针对传统的高层语义提取方法对训练数据集的高度依赖以及准确率不足的问题,提出一种基于Adaboost-SVM的高层次语义概念提取方法,将SVM作为Adaboost的弱分类器训练方法,并充分利用Adaboost对训练数据的平衡及融合弱分类器的特点,提取出高可靠的语义检测器.实验结果表明,与传统方法相比,该方法不仅跨越训练数据不平衡的障碍,而且能够提取出更加可靠的语义检测器.     

动态数据约简的神经网络分类器训练方法研究

针对神经网络分类器训练时间长、泛化能力差的问题,提出了一种基于动态数据约简的神经网络分类器训练方法（DDR）。该训练方法在训练过程中赋给每个训练样本一个权重值作为样本的重要性度量,依据每次网络迭代训练样本的分类错误率动态更新每个训练样本的权重值,之后依据样本的权重值来约简训练样本,从而增加易错分类的边界样本比重,减少冗余核样本的作用。数值实验表明,基于权重的动态数据约简神经网络训练方法不仅大幅缩短了网络的训练时间,而且还能够显著提升网络的分类泛化能力。     

一种基于支持向量机的车型自动分类器设计方案

提出一种基于支持向量机理论的车型分类器的设计方案。通过对实际车辆的图像采集、处理和分析,获取所需样本数据。采用有导师训练方法训练三个支持向量机识别器,使用测试样本对训练出的识别器进行性能测试。最后将三个识别器与表决器结合得到车型分类器。     

基于RSC模型和噪声去除的半监督训练方法

“半监督学习”是利用已经标记好的训练样本和未标记的训练样本一起训练分类器.传统的半监督训练过程对噪声不作辨别,这种做法会因噪声的存在破坏分类器的训练过程,进而影响分类器的分类效果.针对该问题,提出了基于RSC模型和噪声去除的半监督训练方法,在样本训练过程中,使用RSC标签扩展的方法,并添加噪声去除环节.实验表明,该算法能有效降低半监督学习中噪声对分类器的影响,得到更加精确的分类边界,最终提高算法的性能和稳定性.     

基于Bootstrap Averaging的贝叶斯分类算法

针对单词簇上训练朴素贝叶斯文本分类器概率估计偏差较大所导致的分类精度较低问题.在使用概率分布聚类算法得到的单词簇的基础上,根据单词与簇间互信息建立有序单词子序列,采用有放回随机抽样对单词序列构造规模相当的样本集并将估计出的参数的平均值作为训练得到的最终参数对未知文本进行分类.公共文本实验数据集上的实验结果表明,提出的训练方法相对于传统的朴素贝叶斯分类器训练方法能够获得更高的分类精度且过程相对简单.     

嵌入式无线局域网适配器的设计与实现

针对单词簇上训练朴素贝叶斯文本分类器概率估计偏差较大所导致的分类精度较低问题,在概率分布聚类算法得到的单词簇的基础上,根据单词与簇间互信息建立有序单词子序列,采用有放回随机抽样对序列构造规模相当的样本集,并将估计出的参数的平均值作为训练得到的参数对未知文本进行分类.公共文本实验数据集上的实验结果表明,该文提出的训练方法相对于传统的朴素贝叶斯分类器训练方法能够获得更高的分类精度且过程相对简单.     

基于机器学习的KDD-CUP99网络入侵检测数据集的分析

使用Python编程,采用朴素贝叶斯分类器、Softmax回归和决策树回归3种有监督学习算法,对KDD-CUP99网络入侵监测数据集进行训练,并分析结果。首先通过3种分类器库的函数,对KDD-CUP99数据集进行分析预测;然后通过增量式训练方法探究3种分类器对训练数据量的依赖程度;最后通过特征筛选探究3种分类器算法受样本特征数量的影响程度。     

组合SVM分类器在行人检测中的研究

在基于HOG特征的SVM行人检测算法的基础上,提出了组合分类器的改进算法。该算法首先采用多尺度滑动窗口提取HOG特征,并对单个SVM分别进行训练,再将训练好的SVM分别采用串联、并联结构形成新分类器后对行人进行检测。为解决用多尺度滑动窗口提取特征时产生的目标候选区域重叠问题,采用非极大值抑制算法对重叠区域进行融合,进而得到准确候选区。实验表明,组合的SVM分类器可以有效降低误检率和漏检率。     

基于组合特征的Bp神经网络数字识别方法

提出一种组合特征作为Bp神经网络输入层向量实现数字字符识别算法.该算法首先引入了数字字符结构特征中图段特征,并结合数字字符的行列统计特征组合成为新的特征向量;然后根据新的组合特征向量设计Bp神经网络分类器;最后对已有的数字图像样本空间中的训练样本库按照Bp神经网络分类器训练方法进行训练,并对测试样本库中的样本进行识别.根据测试实验,数字字符的识别准确率可达到94%以上.     

A fuzzy classifier with ellipsoidal regions

In this paper, we discuss a fuzzy classifier with ellipsoidal regions which has a learning capability. First, we divide the training data for each class into several clusters. Then, for each cluster, we define a fuzzy rule with an ellipsoidal region around a cluster center. Using the training data for each cluster, we calculate the center and the covariance matrix of the ellipsoidal region for the cluster. Then we tune the fuzzy rules, i.e., the slopes of the membership functions, successively until there is no improvement in the recognition rate of the training data. We evaluate our method using the Fisher iris data, numeral data of vehicle license plates, thyroid data, and blood cell data. The recognition rates (except for the thyroid data) of our classifier are comparable to the maximum recognition rates of the multilayered neural network classifier and the training times (except for the iris data) are two to three orders of magnitude shorter     

Dynamic cluster generation for a fuzzy classifier with ellipsoidalregions

In this paper, we discuss a fuzzy classifier with ellipsoidal regions that dynamically generates clusters. First, for the data belonging to a class we define a fuzzy rule with an ellipsoidal region. Namely, using the training data for each class, we calculate the center and the covariance matrix of the ellipsoidal region for the class. Then we tune the fuzzy rules, i.e., the slopes of the membership functions, successively until there is no improvement in the recognition rate of the training data. Then if the number of the data belonging to a class that are misclassified into another class exceeds a prescribed number, we define a new cluster to which those data belong and the associated fuzzy rule. Then we tune the newly defined fuzzy rules in the similar way as stated above, fixing the already obtained fuzzy rules. We iterate generation of clusters and tuning of the newly generated fuzzy rules until the number of the data belonging to a class that are misclassified into another class does not exceed the prescribed number. We evaluate our method using thyroid data, Japanese Hiragana data of vehicle license plates, and blood cell data. By dynamic cluster generation, the generalization ability of the classifier is improved and the recognition rate of the fuzzy classifier for the test data is the best among the neural network classifiers and other fuzzy classifiers if there are no discrete input variables     

A novel approach to feature selection based on analysis of classregions

This paper presents a novel approach to feature selection based on analysis of class regions which are generated by a fuzzy classifier. A measure for feature evaluation is proposed and is defined as the exception ratio. The exception ratio represents the degree of overlaps in the class regions, in other words, the degree of having exceptions inside of fuzzy rules generated by the fuzzy classifier. It is shown that for a given set of features, a subset of features that has the lowest sum of the exception ratios has the tendency to contain the most relevant features, compared to the other subsets with the same number of features. An algorithm is then proposed that performs elimination of irrelevant features. Given a set of remaining features, the algorithm eliminates the next feature, the elimination of which minimizes the sum of the exception ratios. Next, a terminating criterion is given. Based on this criterion, the proposed algorithm terminates when a significant increase in the sum of the exception ratios occurs due to the next elimination. Experiments show that the proposed algorithm performs well in eliminating irrelevant features while constraining the increase in recognition error rates for unknown data of the classifiers in use.     

基于基因搜索的模糊图象解释方法

图象解决是计算机视觉的重要组成部分，它涉及图象处理，分类器设计和逻辑推理等许多领域。针对目前图象解释系统要面对的严重噪声、模糊性和不确定性问题。重点研究了一种基于基因搜索的双向推理技术，该算法分为如下两步：首先通过基于分割区域统计/几何特征的模式分类器来得到初始的分类模糊隶属度，并根据经验（或统计）得到的先验空间位置关系模糊规则来构造一种有效表达图象解释信息的模糊图。然后通过基因搜索算法融合上面的两类信息来得到图象的最佳解释，实验结果表明，该方法对具有单一对象或多个对象的区域均有很好的效果，也是对基于概率、证据和模糊推理等单向推理机制图象解释方法的提高。     

Segmentation of psoriasis vulgaris images using multiresolution-based orthogonal subspace techniques.

In this paper, a method is proposed for the segmentation of color images using a multiresolution-based signature subspace classifier (MSSC) with application to psoriasis images. The essential techniques consist of feature extraction and image segmentation (classification) methods. In this approach, the fuzzy texture spectrum and the two-dimensional fuzzy color histogram in the hue-saturation space are first adopted as the feature vector to locate homogeneous regions in the image. Then these regions are used to compute the signature matrices for the orthogonal subspace classifier to obtain a more accurate segmentation. To reduce the computational requirement, the MSSC has been developed. In the experiments, the method is quantitatively evaluated by using a similarity function and compared with the well-known LS-SVM method. The results show that the proposed algorithm can effectively segment psoriasis images. The proposed approach can also be applied to general color texture segmentation applications.     

A regression methodology to induce a fuzzy model

In this work, we present the induction of a fuzzy model that represents the behavior of a partial known function. We extend the approach of classical induction of a classifier by building a decision tree, and its generalization for regression problems by CART, to build a fuzzy model. It is defined by a collection of fuzzy regions fixed in the input domain of the function. To obtain fuzzy regions of the input domain, we have defined a new method (FCMD) to get a fuzzy partition of a fuzzy set, which generalizes the classical Bezdeck's method FCM. © 2001 John Wiley & Sons, Inc.     

MPEG VBR video traffic modeling and classification using fuzzytechnique

We present an approach for MPEG variable bit rate (VBR) video modeling and classification using fuzzy techniques. We demonstrate that a type-2 fuzzy membership function, i.e., a Gaussian MF with uncertain variance, is most appropriate to model the log-value of I/P/B frame sizes in MPEG VBR video. The fuzzy c-means (FCM) method is used to obtain the mean and standard deviation (std) of T/P/B frame sizes when the frame category is unknown. We propose to use type-2 fuzzy logic classifiers (FLCs) to classify video traffic using compressed data. Five fuzzy classifiers and a Bayesian classifier are designed for video traffic classification, and the fuzzy classifiers are compared against the Bayesian classifier. Simulation results show that a type-2 fuzzy classifier in which the input is modeled as a type-2 fuzzy set and antecedent membership functions are modeled as type-2 fuzzy sets performs the best of the five classifiers when the testing video product is not included in the training products and a steepest descent algorithm is used to tune its parameters     

Fuzzy classifier design using genetic algorithms

A new method for design of a fuzzy-rule-based classifier using genetic algorithms (GAs) is discussed. The optimal parameters of the fuzzy classifier including fuzzy membership functions and the size and structure of fuzzy rules are extracted from the training data using GAs. This is done by introducing new representation schemes for fuzzy membership functions and fuzzy rules. An effectiveness measure for fuzzy rules is developed that allows for systematic addition or deletion of rules during the GA optimization process. A clustering method is utilized for generating new rules to be added when additions are required. The performance of the classifier is tested on two real-world databases (Iris and Wine) and a simulated Gaussian database. The results indicate that highly accurate classifiers could be designed with relatively few fuzzy rules. The performance is also compared to other fuzzy classifiers tested on the same databases.     

DESIGN OF A FUZZY MIN-MAX HYPERBOX CLASSIFIER USING A SUPERVISED LEARNING METHOD

ABSTRACT

This investigation proposes a fuzzy min-max hyperbox classifier to solve M-class classification problems. In the proposed fuzzy min-max hyperbox classifier, a supervised learning method is implemented to generate min-max hyperboxes for the training patterns in each class so that the generated fuzzy min-max hyperbox classifier has a perfect classification rate in the training set. However, the 100% correct classification of the training set generally leads to overfitting. In order to improve this drawback, a procedure is employed to decrease the complexity of the input decision boundaries so that the generated fuzzy hyperbox classifier has a good generalization performance. Finally, two benchmark data sets are considered to demonstrate the good performance of the proposed approach for solving this classification problem.     

An efficient fuzzy classifier with feature selection based on fuzzyentropy

This paper presents an efficient fuzzy classifier with the ability of feature selection based on a fuzzy entropy measure. Fuzzy entropy is employed to evaluate the information of pattern distribution in the pattern space. With this information, we can partition the pattern space into nonoverlapping decision regions for pattern classification. Since the decision regions do not overlap, both the complexity and computational load of the classifier are reduced and thus the training time and classification time are extremely short. Although the decision regions are partitioned into nonoverlapping subspaces, we can achieve good classification performance since the decision regions can be correctly determined via our proposed fuzzy entropy measure. In addition, we also investigate the use of fuzzy entropy to select relevant features. The feature selection procedure not only reduces the dimensionality of a problem but also discards noise-corrupted, redundant and unimportant features. Finally, we apply the proposed classifier to the Iris database and Wisconsin breast cancer database to evaluate the classification performance. Both of the results show that the proposed classifier can work well for the pattern classification application.