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Description: bayeserr - Computes the Bayesian risk for optimal classifier.
％ bayescln - Classifier based on Bayes decision rule for Gaussians.
％ bayesnd - Discrim. function, dichotomy, max aposteriori probability.
％ bhattach - Bhattacharya s upper limit of mean class. error.
％ pbayescln - Plots discriminat function of Bayes classifier.-bayeserr - Computes the Bayesian risk for o ptimal classifier. % bayescln - Classifier bas ed on Bayesian decision rule for Gaussians. Bayes% nd - Discrim. function, dichotomy. max aposteriori probability. % bhattach - Bhat tacharya s upper limit of mean class. error. pb% ayescln - Plots discriminat function of Bayes c lassifier.
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Size: 5302 |
Author: 孟庆 |
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Description: This paper presents a novel segmentation algorithm for metallographic images, especially those objects without regular boundaries and homogeneous intensity. In metallographic quantification, the complex microstructures make the conventional approaches hard to achieve a satisfactory partition. We formulate the segmentation procedure as a new framework of iterative watershed region growing constrained by the ridge information. The seeds are selected by an effective double-threshold approach, and the ridges are superimposed as the highest waterlines in watershed transform. To tackle the over-segmentation problem, the blobs are merged iteratively with the utilization of Bayes classification rule. Experimental results show that the algorithm is effective in performing segmentation without too much parameter tuning.
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Size: 712264 |
Author: jjrs05 |
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Description: This paper presents a novel segmentation algorithm for metallographic images, especially those objects without regular boundaries and homogeneous intensity. In metallographic quantification, the complex microstructures make the conventional approaches hard to achieve a satisfactory partition. We formulate the segmentation procedure as a new framework of iterative watershed region growing constrained by the ridge information. The seeds are selected by an effective double-threshold approach, and the ridges are superimposed as the highest waterlines in watershed transform. To tackle the over-segmentation problem, the blobs are merged iteratively with the utilization of Bayes classification rule. Experimental results show that the algorithm is effective in performing segmentation without too much parameter tuning.
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Size: 712264 |
Author: jjrs05 |
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Description: bayeserr - Computes the Bayesian risk for optimal classifier.
％ bayescln - Classifier based on Bayes decision rule for Gaussians.
％ bayesnd - Discrim. function, dichotomy, max aposteriori probability.
％ bhattach - Bhattacharya s upper limit of mean class. error.
％ pbayescln - Plots discriminat function of Bayes classifier.-bayeserr- Computes the Bayesian risk for o ptimal classifier. % bayescln- Classifier bas ed on Bayesian decision rule for Gaussians. Bayes% nd- Discrim. function, dichotomy. max aposteriori probability. % bhattach- Bhat tacharya s upper limit of mean class. error. pb% ayescln- Plots discriminat function of Bayes c lassifier.
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Size: 5120 |
Author: 孟庆 |
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Description: 数据挖掘中的bayes算法,很好的代码，可以挖掘数据库中的bayes规则，很有效哦！-Bayes data mining algorithm, a very good code, you can tap the database of the Bayes rule, very effective, oh!
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Size: 99328 |
Author: 王路漫 |
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Description: 电子书，一篇论文，----用于分类规则挖掘的贝叶斯信念构造算法-For Classification Rule Mining Algorithm for Constructing Bayesian Belief
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Size: 39936 |
Author: 啊 |
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Description: Basic theory of LDPC decoder theory
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Size: 206848 |
Author: jerry |
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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.
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Size: 1310720 |
Author: 张广明 |
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Description: 本程序所采用的基于栅格的地图表示方法即将整个环境离散化为规则的基本单元，二维栅格，对于每个栅格指出其中是否存在障碍物，通过对栅格的描述实验环境的建模。目前，这种方法已经在许多机器人系统中得到应用，是使用较为成功的一种方法。栅格地图的优点是：易于创建和维护，且不需要明确的几何参数。栅格地图的缺点是：当栅格数量增大时(在大规模环境或对环境划分比较详细时)，对于地图的维护所占用的内存和CPU时间迅速增长，使计算机的实时处理变得很困难，也就是说刚栅格表示的环境地图，环境空间的分辨率与栅格尺寸的大小有关，增加分辨率将要增加运算的时间和空间复杂度。-It uses a method based on sonar sensors to create a grid map, the Bayes rule to create map of mobile robot, on a number of sonar sensor data fusion to resolve the conflict between the information to improve the accuracy of the map plane built.
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Size: 10082304 |
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Description: 此文档研究人工智能中的不确定性问题，以及概率问题，并且提到Bayes法则。-This document research of uncertainty in artificial intelligence, and the problem of probability, and mentioned that Bayes rule.
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Size: 576512 |
Author: 林靖雄 |
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Description: This the code for bayes classifier. It gets the feature vectors from two dimentional data and classifies them based on bayes rule!-This is the code for bayes classifier. It gets the feature vectors from two dimentional data and classifies them based on bayes rule!
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Size: 8192 |
Author: Soroush |
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Description: 机器学习领域经典分类算法综述，包括Decision Tree（ＩＤ３、C4.5(C5.0)、CART、PUBLIC、SLIQ和SPRINT算法），三种典型贝叶斯分类器（朴素贝叶斯算法、TAN算法、贝叶斯网络分类器），k-近邻 、 基于数据库技术的分类算法（ MIND算法、GAC-RDB算法），基于关联规则（CBA:Classification Based on Association Rule）的分类（Apriori算法），支持向量机分类，基于软计算的分类方法（粗糙集（rough set）、遗传算法、模糊逻辑、人工神经网络算法）。-Classical machine learning classification algorithms overview。 Including Decision Tree (ID3, C4.5 (C5.0), CART, PUBLIC, SLIQ and SPRINT algorithm), three typical Bayesian classifier (Naive Bayes algorithm, TAN algorithm Bayesian network classifiers), k-nearest neighbor based classification algorithm (MIND algorithm, GAC-RDB algorithms) database technology, based on association rules (CBA: classification (Apriori algorithm) Classification Based on Association Rule), and support vector machine classification, classification method based on soft computing (rough sets (rough set), genetic algorithms, fuzzy logic, artificial neural network algorithm)....
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Size: 30720 |
Author: MM |
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Description: Rotational Linear Discriminant Analysis Using Bayes Rule for Dimensionality Reduction
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Size: 119808 |
Author: Nima |
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Description: 简单的机器人定位问题，基于经典的贝叶斯滤波器算法。-Simple Robot With Bayes Rule
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Size: 3072 |
Author: 王元军 |
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