m4-latest.tar

matlab写的bp神经网络对mnist数据集进行学习分类(Learning and classification of MNIST data sets based on BP neural network written by MATLAB)

提取语音信号的MFCC参数进行语音信号的识别，利用神经网络，自带有训练样本和测试样本(Identification of MFCC parameters of speech signal extraction of speech signal, using neural network, with the training samples and test samples)

基于栅格地图的机器人模糊地图创建方法的介绍知识。(Raster map-based robot fuzzy map to create a method of introduction of knowledge.)

获得本机IP地址的例子程序源码.- Obtains this machine IP address the example procedure source code

精通Java网络编程中的 sockat 编程源代码(Proficient in Java Network Programming sockat programming source code)

说明：  matlab code of wsn master

支持向量机SVM(Support Vector Machine）作为一种可训练的机器学习方法，依靠小样本学习后的模型参数进行导航星提取，可以得到分布均匀且恒星数量大为减少的导航星表。 基本情况　Vapnik等人在多年研究统计学习理论基础上对线性分类器提出了另一种设计最佳准则。其原理也从线性可分说起，然后扩展到线性不可分的情况。甚至扩展到使用非线性函数中去，这种分类器被称为支持向量机（Support Vector Machine，简称SVM）。支持向量机的提出有很深的理论背景。 支持向量机方法是在近年来提出的一种新方法。(Support Vector Machine SVM (Support Vector Machine) as a trainable machine learning methods, relying on small samples after learning Navstar extract model parameters can be distributed uniformly and greatly reduced the number of stars the guide star. Basic Vapnik et al in years of study on the basis of statistical learning theory, linear classifier proposed an alternative design best practices. The principle is also linearly separable from talking, and then extended to the case of non-linear. Even extended to use a nonlinear function to go, this classification is known as support vector machines (Support Vector Machine, referred SVM). Support vector machine made a deep theoretical background. Support Vector Machine method is a new method proposed in recent years.)

光伏发电量神经网络预测练习，光伏因素数据的提取，归一化处理以及预测(Photovoltaic power generation neural network forecasting exercise, the photovoltaic element data extraction, normalization and prediction)

SVM支持向量机多分类器源码，用过的，绝对好用(SVM SVM classifier source, used absolutely everything)

免注册QQ/MSN号码,直接在网页上聊天/通话/视频,最佳在线客服.(Live Support)