Description: 一篇关于SCM的综述性文章，SCM是一种比SVM分类性能更优秀的分类器。以后还会上传几篇有关SCM的文章-on SCM's a review article, SCM than SVM is a better classification performance classifier. After a few will upload the articles SCM Platform: |
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Author:ckb |
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Description: adaboost 弱分类器构成强分类器算法，并作图，分析样本数对性能的影响-constitute a weak classifier AdaBoost strong classifier algorithm, and mapping, analysis of sample size on the performance of Platform: |
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Author:毛楠 |
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Description: 模式识别课件
当预先不知道类型数目，或者用参数估计和非参数估计难以确定不同类型的类概率密度函数时，为了确定分类器的性能，可以利用聚类分析的方法。-When the pre-recognition software does not know the type of number, or parameter estimation and non-parameter estimation it is difficult to determine the different types of categories of probability density function, in order to determine the classifier performance, can make use of cluster analysis method. Platform: |
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Author:gpscar |
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Description: 为了测试评估贝叶斯分类器的性能,用不同数据集进行对比实验是必不可少的. -In order to test the assessment of the performance of Bayesian classifier, and compare different experimental data sets is essential. Platform: |
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Author:马英豪 |
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Description: ROC curves illustrate performance on a binary classification problem where classification is based on simply thresholding a set of scores at varying levels. Lenient thresholds give high sensitivity but low specificity, strict thresholds give high specificity but low sensitivity the ROC curve plots this trade-off over a range of thresholds (usually with sens vs 1-spec, but I prefer sens vs spec this code gives you the option).
It is theoretically possible to operate anywhere on the convex hull of an ROC curve, so this is plotted too. The area under the curve (AUC) for a ROC plot is a measure of overall accuracy, and the area under the ROCCH is a kind of upper bound on what might be achievable with a weighted combination of differently thresholded results from the given classifier
-ROC curves illustrate performance on a binary classification problem where classification is based on simply thresholding a set of scores at varying levels. Lenient thresholds give high sensitivity but low specificity, strict thresholds give high specificity but low sensitivity the ROC curve plots this trade-off over a range of thresholds (usually with sens vs 1-spec, but I prefer sens vs spec this code gives you the option).
It is theoretically possible to operate anywhere on the convex hull of an ROC curve, so this is plotted too. The area under the curve (AUC) for a ROC plot is a measure of overall accuracy, and the area under the ROCCH is a kind of upper bound on what might be achievable with a weighted combination of differently thresholded results from the given classifier
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Author:saadat |
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Description: 贝叶斯分类器的设计，其中包括协方差相等与不等时的两类情况，分类效果很好-Bayesian classifier design, including equal and unequal covariance of two categories, very good classification performance Platform: |
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Author:邓俊俊 |
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Description: 用其中一半的数据采用ANN-BP算法设计分类器，另一半数据用于测试分类器性能。-Half of the data used by ANN-BP algorithm design classifier, the other half of the data used to test the classifier performance. Platform: |
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Author:龚煜 |
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Description: ANN-BP分类器设计，用其中一半的数据采用ANN-BP算法设计分类器，另一半数据用于测试分类器性能。-ANN-BP classifier design, with half of the data using ANN-BP algorithm design classifier, the other half of the data used to test the classifier performance. Platform: |
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Author:龚煜 |
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Description: 朴素贝叶斯分类是一种简单而高效的分类模型，然而条件独立性假设在现实中很少出，致使其性能有所下降。通过引入关联规则，从两方面来改善朴素贝叶斯分类的性能。一方面，通过对关联规则的挖掘，发现条件属性之间的关联关系，并且利用这种关联关系弱化朴素贝叶斯的独立性假设；另一方面，通过关联规则的置信度，给朴素贝叶斯加权。 -Naive Bayesian classifier is a simple and efficient classification model, the conditional independence assumption, however, rarely in the real world, resulting in decreased performance. Through the introduction of association rules, two ways to improve the performance of naive Bayesian classifier. On the one hand, by association rule mining, found the association between condition attributes and use this association weakened Bayesian independence assumption the other hand, by association rule confidence, to the simple Bayesian Alaska right. Platform: |
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Author:张广明 |
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Description: 对“data1.m”数据，分别采用感知机算法、最小平方误差算法、线性SVM算法设计分类器，分别画出决策面，并比较性能。-The "data1.m" data, respectively, using the perceptron algorithm, the least square error algorithm, the linear SVM algorithm design classifier, respectively, to draw the decision-making surface, and compare performance. Platform: |
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Author:刘攀 |
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Description: 对“data2.m”数据，用其中一半的数据采用ANN-BP算法设计分类器，另一半数据用于测试分类器性能。-The "data2.m" data, which half of the data using the ANN-BP algorithm design classifiers, the other half of the data used to test the classifier performance. Platform: |
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Author:刘攀 |
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Description: 对“data3.m”数据，用其中一半的数据采用非线性SVM算法设计分类器并画出决策面，另一半数据用于测试分类器性能。采用三套核函数，并且比较不同核函数的结果。-To "data3.m" data, which half of the data using nonlinear SVM classification algorithm design and draw the decision-making surface, the other half of the data used to test the classifier performance. Three sets of kernel function, and compare the results of the different kernel functions. Platform: |
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Author:刘攀 |
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Description: 对“data2.m”数据，用其中一半的数据采用ANN-BP算法设计分类器，另一半数据用于测试分类器性能。-The " data2.m" data, which half of the data using the ANN-BP algorithm design classifiers, the other half of the data used to test the classifier performance. Platform: |
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Author:孙琴 |
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Description: 对朴素贝叶斯算法的进一步改进。朴素贝叶斯分类器是一种简单而高效的分类器，但是它的属性独立性假设使其无法表示现实世界属性之间的依赖关系，以及它的被动学习策略，影响了它的分类性能。本文从不同的角度出发，讨论并分析了三种改进朴素贝叶斯分类性能的方法。为进一步的研究打下坚实的基础-Naive Bayes algorithm further improved. Naive Bayes classifier is a simple and efficient classifier, but its attribute independence assumption it can not be said that the dependencies between the properties of the real world, as well as the passive learning strategies, affect its classification performance. Starting from different points of view to discuss and analyze the three improved the performance of the naive Bayesian classifier. To lay a solid foundation for further study Platform: |
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Author:xujingxue |
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Description: 本文从不同的角度出发，讨论并分析了三种改进朴素贝叶斯分类性能的方法。为进一步的研究打下坚实的基础。-In this paper, starting from a different perspective, to discuss and analyze the three improved Naive Bayesian classifier performance. Lay a solid foundation for further research. Platform: |
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Author:houying |
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Description: 《MATLAB神经网络30个案例分析》中的第13个例子，案例13 SVM神经网络中的参数优化---提升分类器性能。希望对大家有一定的帮助！-The MATLAB neural network analysis of 30 cases of example, 13 cases of 13 SVM parameters optimization of neural network classifier performance- ascension. Hope to have certain help to everybody!
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Author:杨飞 |
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Description: 显示一系列分类器的性能，对目标类数据进行分类，对这两类数据进行分类-Displays a series of classifier performance, the target class data classification, these two types of data classification Platform: |
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Author: |
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Description: 粒子群优化算起源于对鸟群、鱼群以及对某些社会行为的模拟，是一种基于群体智能的进化计算技术。而小生境技术则起源于遗传算法，这种方法能使基于群体的随机优化算法形成物种，从而使相应的优化算法具有发现多个最优解的能力。而多分类器集成技术则是通过多个分类器进行某种组合来决定最终的分类，以取得比单个分类器更好的性能。多分类器集成技术要求基元分类器不仅个体性能要好并且其差异度要大，这与小生境技术形成物种的能力具有很多内在的相似性。目前己经有研究者将小生境技术应用于多分类器集成，但由于传统的小生境技术仍然不完善，存在一些内在的陷，因而这些应用还不成熟和完善。
(Particle swarm optimization (partieleSwarmOptimization) originated in the birds, fish, and of a
Some simulation of social behavior, is a swarm intelligence-based evolutionary computing. The origin of the niche technology is In genetic algorithms, this method can make random optimization algorithm based on the formation of groups of species, so that the appropriate priority
Algorithm has the ability to find multiple optimal solutions. The integration technology of multiple classifiers is through multiple classifiers into
Some combination of the line to determine the final classification, in order to obtain better than a single classifier performance. Integration of multiple classifiers
Technical requirements for primitive classification is not only better individual performance and the difference to a large degree, which form a niche technology The ability of species has many inherent similarities. The researchers will now have a niche technology used in multisection
Class ens) Platform: |
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Author:dreamer |
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Search - classifier performance