Are you the Spring* Framework advocator? We can explore those common parts, whichever the Spring* are implemented by Java, dotNET, Ruby or others.
1. Spirited community (No matter it is Generic Programing Language or Dynamic Language), you should bet more guys will be interested in that for the future growing.
2. Inversion of Control - You are allowed to define the blue prints for wiring classes together. Considering using XML configuration to decorate them.
3. Aspect Oriented Programming - You can wrap advices and objects by this way, the smart usage are about remoting, debugger, and performance tracing.
4. Data Access - Reading from the database requires a monotonous cycle of opening cursors, reading rows and closing cursors, along with exception handlers. With the template class, SQL query is just enough.
5. Transaction Management - It can provides multiple ways to more readily manage wrapping business logic with transactions.
6. Security - It should provide plug-in support security interceptors to lock down access to your methods.
7. Remoting - It is easy to convert your local application into a distributed one, if you have already built your client and server pieces using the IoC container, then rely on the configuration change to go from local to distributed.
8. Plug-ins: Use the plug-in system designed to help you rapidly develop applications.
分享到:
相关推荐
data likelihood, but also the higher will be the computational burden and data overfitting. In this work we propose a clustering method based on the expectation maximization algorithm that adapts on-...
《Maximum Likelihood from Incomplete Data via the EM Algorithm》是一篇由A.P. Dempster、N.M. Laird和D.B. Rubin发表于1977年的经典论文,该论文首次系统地介绍了EM算法,并详细探讨了其在处理不完整数据时的...
《Maximum Likelihood from Incomplete Data via the EM Algorithm》是一篇由A. P. Dempster、N. M. Laird和D. B. Rubin在1977年发表的重要论文,该论文首次系统地介绍了EM算法(Expectation-Maximization algorithm...
彼得·J·胡贝尔(Peter J. Huber)在其论文《非标准条件下最大似然估计的行为》中讨论了在非标准情况下最大似然估计(ML估计)的性质。该研究针对最大似然估计的传统假设做了放宽,并证明了最大似然估计在较弱条件...
异方差高斯混合的广义似然比检验的计算,姜文华,,设${X_i,ile n}$是服从$X_isim N( heta_i, au_i^2)$的异方差正态观测,其中均值和方差都未知,且$sigma>0$是未知标准差的一个已知下界。记$f_n(x)=sum_{i=
If the table has a clustered index, all columns of the clustered key will be duplicated in the nonclustered index leaf rows, unless there is overlap between the clustered and nonclustered key....
在MATLAB环境中,loglikelihood(对数似然函数)是一个重要的统计概念,广泛应用于概率模型的参数估计,尤其是在机器学习和统计建模中。对数似然函数是似然函数取对数后的形式,它简化了计算过程并有助于解决优化...
• Maximum Likelihood Outlier Detection (MLOD) is an inlier-based outlier detection algorithm. The problem of inlier-based outlier detection is to find outliers in a set of samples (called the ...
Finally, a fast and linear strategy, which computes the log-likelihood ratio (LLR) between same versus different speakers hypotheses, scores the verification trials. The Identity toolbox provides ...
What you will learn from this book, Learn how to build a FIX protocol parser, Calibrate counting processes on real data, Estimate model parameters using the Maximum Likelihood Estimation method, Use ...
本文介绍了一种最大似然立体匹配算法(Maximum Likelihood Stereo, MLS),这是一种应用于计算机视觉领域的算法,旨在通过优化最大似然代价函数来实现立体匹配。算法的核心在于其假设左右图像中的对应特征围绕共同的...
经验似然(Empirical Likelihood,EL)是数理统计领域的一种重要方法,它在处理估计问题时提供了一种无须指定模型先验分布的框架。这种方法由Peter Hall在1980年代提出,是对传统最大似然估计的扩展,尤其在面对小...
非局部最大似然估计方法(Nonlocal Maximum Likelihood Estimation Method)主要用于医学图像处理领域,特别是针对磁共振成像(MR Images)中的Rician噪声进行降噪处理。Rician噪声是MRI图像中常见的一种噪声类型,...
Profile Likelihood 理解 统计学中,Profile Likelihood 是一种重要的参数估计方法。本文将对 Profile Likelihood 的理解进行详细的阐述。 Profile Likelihood 的方法 Profile Likelihood 的方法是将未知参数 ...
Finally, you'll be making relevant information easily available that quantifies what a great job you've been doing, including the number of hours that volunteers gave to your organization last year, ...
The SSM (Spring, SpringMVC, MyBatis) framework is a popular choice for developing web applications due to its modularity, scalability, and ease of use. Spring provides dependency injection and manages...
### 似然函数 Likelihood Function #### 一、似然函数的概念 在机器学习与统计学领域中,似然函数(Likelihood Function)是一个至关重要的概念。它主要用于度量一个给定模型参数θ下的数据观测值X的可能性。简单...