Description: 一种基于最大熵和部件的物体检测方法。在人脸检测上的应用取得了很大的准确率提升。-This paper presents a probabilistic part-based approach for texture and object recognition. Textures are represented using a part dictionary found by quantizing the appearance
of scale- or affine-invariant keypoints. Object classes are represented using a dictionary of composite semi-local parts, or groups of neighboring keypoints with stable and
distinctive appearance and geometric layout. A discriminative
maximum entropy framework is used to learn the posterior
distribution of the class label given the occurrences
of parts from the dictionary in the training set. Experiments
on two texture and two object databases demonstrate the
effectiveness of this framework for visual classification. Platform: |
Size: 2328576 |
Author:刘馨惠 |
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Description: 介绍了一种基于词网的最大似然线性回归无监督自适应算法，并进行了改进。根据解码得到的词网估计变换参数，词网的潜在误识率远小于识别结果，因此可以使参数估计更为准确。传统的一个很大缺点是计算量极大，较难实用，对此本文提出了两个改进技术：1利用后验概率压缩词网；2利用单词的时间信息限制状态统计量的计算范围。实验测定,误识率比传统相对下降了。-Introduced the term network based maximum likelihood linear regression unsupervised adaptive algorithm, and an improved. According to decode the received word net estimated transformation parameters, the word error rate of net potential is far less than the recognition results, it can make parameter estimation more accurate. A major drawback is that the traditional calculation enormously difficult practical, this paper presents two improved technology: 1 compression using word posterior probability network 2 time information using the word limit state statistic calculation. Experimental determination of the relative error rate than traditional down. Platform: |
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Author:自然快乐 |
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Description: ：自适应技术在近年来得到越来越多的重视，其中应用广泛的包括,-.、,//0，该技术利用少量特定
人数据就可以调整码本，快速地提升识别性能，它要求原始的码本有很好的说话人无关性。本文介绍了结合
,//0 自适应的说话人自适应训练（1234536 -74289:3 649<9<=，以下简称1- ）算法，这种方法将每个说话人码本
视为说话人无关码本经过线性变换的结果，在此基础上训练的说话人无关码本更有效剔除了说话人相关信
息，因此在说话人自适应中时能根据特定数据调整更好地逼近说话人特性，从而有更好的性能表现。-Introduced the term network based maximum likelihood linear regression unsupervised adaptive algorithm, and an improved. According to decode the received word net estimated transformation parameters, the word error rate of net potential is far less than the recognition results, it can make parameter estimation more accurate. A major drawback is that the traditional calculation enormously difficult practical, this paper presents two improved technology: 1 compression using word posterior probability network 2 time information using the word limit state statistic calculation. Experimental determination of the relative error rate than traditional down. Platform: |
Size: 220160 |
Author:自然快乐 |
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Description: 汽车高斯曲面拟合
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2程序，以适应到表面二维高斯：
子= A *的进出口（ -（（西为X0）^2/2/sigmax^2 +（艺Y0的）^2/2/sigmay^ 2）。。）+ b的
这些例程是自动在某种意义上说，他们并不需要出发对模型参数的猜测规范。
autoGaussianSurfML（十一，彝，子）适合通过对模型参数的最大似然（最小二乘）。它首先计算了该模型在许多可能的参数值，然后选择最佳质量设置和细化与lsqcurvefit它。
autoGaussianSurfGS（十一，彝，紫）的估计，通过指定数据的贝叶斯生成模型，然后采取通过从模型吉布斯抽样样本后ofthis模型参数。这种-Auto Gaussian Surface fit
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2 routines to fit a 2D Gaussian to a surface:
zi = a*exp(-((xi-x0).^2/2/sigmax^2+ (yi-y0).^2/2/sigmay^2))+ b
The routines are automatic in the sense that they do not require the specification of starting guesses for the model parameters.
autoGaussianSurfML(xi,yi,zi) fits the model parameters through maximum likelihood(least-squares). It first evaluates the quality of the model at many possible values of the parameters then chooses the best set and refines it with lsqcurvefit.
autoGaussianSurfGS(xi,yi,zi) estimates the model parameters by specifying a Bayesian generative model for the data, then taking samples from the posterior ofthis model through Gibbs sampling. This method is insensitive to local minimain posterior and gives meaningful error bars (Bayesian confidence intervals) Platform: |
Size: 7168 |
Author:zzskzcau |
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Description: 贝叶斯分类器的分类原理是通过某对象的先验概率，利用贝叶斯公式计算出其后验概率，即该对象属于某一类的概率，选择具有最大后验概率的类作为该对象所属的类-Bayesian classifier principle a priori probability of the object using the Bayesian formula to calculate the subsequent posterior probability that the object belongs to a certain class of probability, select the class with the maximum a posteriori as the object belongs to class Platform: |
Size: 1024 |
Author:冰点 |
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Description: 针对概率假设密度 (Probability hypothesis density, PHD) 高斯混合实现算法中的分量删减问题, 提出了基于
Dirichlet 分布的分量删减算法以改进概率假设密度高斯混合实现算法的性能. 算法采用极大后验准则估计混合参数, 采用仅
依赖于混合权重的负指数 Dirichlet 分布作为混合参数的先验分布, 利用拉格朗日乘子推导了混合权重的更新公式. 算法利用
负指数 Dirichlet 分布的不稳定性, 在极大后验迭代过程中驱使与目标强度不相关的分量消亡. 该不稳定性还能够解决多个相
近分量共同描述一个强度峰值的问题-As far as component pruning in Gaussian mixture (GM) implementation of probability hypothesis density
(PHD) is concerned, a component pruning algorithm based on Dirichlet distribution is proposed to improve the performance
of Gaussian mixture implementation of probability hypothesis density. The maximum a posterior criterion is adopted for
estimation of mixing parameters. Dirichlet distribution with negative exponent parameters, which only depends on mixing
weights, is adopted as the prior distribution of mixing parameters. The update formulation of mixing weight is derived by
Lagrange multiplier. The instability of Dirichlet distribution with negative exponent parameters is applied to driving the
components irrelevant with target intensity to extinction during the maximum a posterior iteration. Besides, the problem
that one peak of intensity is presented by several proximate mixing component Platform: |
Size: 1289216 |
Author:正东 |
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Description: 利用拉格朗日乘子推导了混合权重的更新公式. 算法利用
负指数 Dirichlet 分布的不稳定性, 在极大后验迭代过程中驱使与目标强度不相关的分量消亡. 该不稳定性还能够解决多个相
近分量共同描述一个强度峰值的问题, 有利于后续多目标状态的提取. 仿真结果表明, 基于 Dirichlet 分布的分量删减算法优
于典型高斯混合实现中的删减算法-The update formulation of mixing weight is derived by
Lagrange multiplier. The instability of Dirichlet distribution with negative exponent parameters is applied to driving the
components irrelevant with target intensity to extinction during the maximum a posterior iteration. Besides, the problem
that one peak of intensity is presented by several proximate mixing component, can be solved by this instability. It is
useful for the following state extraction. Simulation results show that the component pruning algorithm based on Dirichlet
distribution is superior to that of typical Gaussian mixture implementation Platform: |
Size: 701440 |
Author:正东 |
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Description: 基于最大后验概率的超分辨图像复原，能够较好的实现频谱外推，实验结果正确-Super-resolution image restoration based on maximum posterior probability, it is possible to achieve a better spectrum extrapolation, experimental results are correct Platform: |
Size: 5120 |
Author:李思萌 |
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Description: 计算基于最大后验的正则最小二乘，基于该程序可选择正则最小二乘的正则系数及基函数-Calculated based on the maximum a posteriori the regularized least squares Platform: |
Size: 1024 |
Author:付瑶 |
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Description: maximum a posterior estimator of magnitude squared speech and also using mmse of SNR uncertainity and many other. Platform: |
Size: 45056 |
Author:nidhusha |
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Description: In Bayesian statistics, a maximum a posteriori probability (MAP) estimate is a mode of the posterior distribution. Platform: |
Size: 2048 |
Author:farshid |
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Description: 本文以拼写检查作为例子，讲解Naive Bayes分类器是如何实现的。对于用户输入的一个单词（words），拼写检查试图推断出最有可能的那个正确单词（correct）。当然，输入的单词有可能本身就是正确的。比如，输入的单词thew，用户有可能是想输入the，也有可能是想输入thaw。为了解决这个问题，Naive Bayes分类器采用了后验概率P(c|w)来解决这个问题。P(c|w)表示在发生了w的情况下推断出c的概率。为了找出最有可能c，应找出有最大值的P(c|w)，即求解问题-In this paper, spell check as an example to explain the Naive Bayes classifier is implemented. For a user to enter a word (words), the spelling checker tries to infer the most likely the correct word (correct). Of course, it is possible to enter the word itself is correct. For example, enter the word thew, users may want to enter the, there may be trying to enter the thaw. After To solve this problem, Naive Bayes classifier using a posterior probability P (c | w) to solve this problem. The P (c | w) represents the case of the probability of w c is inferred. In order to identify the most likely c, you should find out the maximum value of P (c | w), that is, to solve the problem Platform: |
Size: 1024 |
Author:王志坦 |
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Description: 贝叶斯分类器，通过某对象的先验概率，利用贝叶斯公式计算出其后验概率，即该对象属于某一类的概率，选择具有最大后验概率的类作为该对象所属的类。-Bias classifier, by a priori probability of an object, using the Bias formula to calculate the posterior probability, the probability that the object belongs to a certain category, with maximum posterior probability as the objects belonging to the class.
Platform: |
Size: 7247872 |
Author:Christiana |
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Search - maximum a posterior