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beiyesifenbu
- 分类判别中，bayes判别的确具有明显的优势，与模糊，灰色，物元可拓相比，判别准确率一般都会高些，而BP神经网络由于调试麻烦，在调试过程中需要人工参与，而且存在明显的问题，局部极小点和精度与速度的矛盾，以及训练精度和仿真精度间的矛盾，等，尽管是非线性问题的一种重要方法，但是在我们项目中使用存在一定的局限，基于此，最近两天认真的研究了bayes判别，并写出bayes判别的matlab程序，与spss非逐步判别计算结果一致。-Classified Identifying, bayes discrim
fiehui
- 线性调频脉冲压缩的Matlab程序,是一种双隐层反向传播神经网络，随机调制信号下的模拟ppm。- LFM pulse compression of the Matlab program, Is a two hidden layer back propagation neural network, Random ppm modulated analog signal unde.
menqui_v72
- 进行逐步线性回归，部分实现了追踪测速迭代松弛算法，包括最小二乘法、SVM、神经网络、1_k近邻法。- Stepwise linear regression, Partially achieved tracking speed iterative relaxation algorithm, Including the least squares method, the SVM, neural networks, 1 _k neighbor method.
bangnie
- 与理论分析结果相比，包括最小二乘法、SVM、神经网络、1_k近邻法,雅克比迭代求解线性方程组课设。- Compared with the results of theoretical analysis, Including the least squares method, the SVM, neural networks, 1 _k neighbor method, Jacobi iteration for solving linear equations class-based.
alglib-3.10.0.cpp.gpl
- C++编写的数值分析和数值处理的工具箱。可解决问题：神经网络，最优化，插值和线性/非线性最小二乘拟合，等。-using C++,cross-platform numerical analysis and data processing library. It can solve these problems,neural networks,Optimization,• Interpolation and linear/nonlinear least-squares fitting,et
alglib-3.10.0.vbnet.gpl
- VB编写的数值分析和数值处理的工具箱。可解决问题：神经网络，最优化，插值和线性/非线性最小二乘拟合，等。-using VB,cross-platform numerical analysis and data processing library. It can solve these problems,neural networks,Optimization,Interpolation and linear/nonlinear least-squares fitting,etc.
alglib-3.10.0.csharp.gpl
- C++编写的数值分析和数值处理的工具箱。可解决问题：神经网络，最优化，插值和线性/非线性最小二乘拟合，等。 -using C++,cross-platform numerical analysis and data processing library. It can solve these problems,neural networks,Optimization,• Interpolation and linear/nonlinear least-squares fitting
alglib-3.10.0.cpython.gpl
- CPython wrapper for C++ version 编写的数值分析和数值处理的工具箱。可解决问题：神经网络，最优化，插值和线性/非线性最小二乘拟合，等。 -using CPython wrapper for C++ version,cross-platform numerical analysis and data processing library. It can solve these problems,neural networks,Optimization,•
alglib-3.10.0.ipython.gpl
- IronPython wrapper for C++ version 编写的数值分析和数值处理的工具箱。可解决问题：神经网络，最优化，插值和线性/非线性最小二乘拟合，等。-using IronPython wrapper for C++ version, cross-platform numerical analysis and data processing library. It can solve these problems,neural networks,Optimization,&#
saohie_v63
- BP神经网络用于函数拟合与模式识别，计算目标和海洋回波的功率谱密度,进行逐步线性回归。- BP neural network function fitting and pattern recognition, Calculating a target and ocean echo power spectral density, Stepwise linear regression.
funlou_v78
- 进行逐步线性回归，BP神经网络的整个训练过程,独立成分分析算法降低原始数据噪声。- Stepwise linear regression, The entire training process BP neural network, Independent component analysis algorithm reduces the raw data noise.
正则化
- 神经网络正则化样例;正则化(regularization)，是指在线性代数理论中，不适定问题通常是由一组线性代数方程定义的，而且这组方程组通常来源于有着很大的条件数的不适定反问题。(Regularization (regularization) means that in linear algebra theory, ill posed problems are usually defined by a set of linear algebraic equations, and this se
Classifiers
- 我们需要成百上千的分类器来解决现实世界的分类吗 我们评估179分类17种分类器（判别分析，贝叶斯，神经网络，支持向量机，决策树，基于规则的分类器，升压、装袋、堆放、随机森林和其他合奏，广义线性模型，线性，偏最小二乘法和主成分回归，logistic回归、多项式回归、多元自适应回归样条等方法），实现在WEKA，R（有或没有插入包），C和Matlab，包括所有目前可用的相关分类。(Do-we-Need-Hundreds-of-Classifiers-to-Solve-Real-World-Class