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使用Logistic Regression来讲解Probabilistic Classification Methods并绘出正确图表(Logistic Regression to explain the use of Probabilistic Classification Methods and draw the correct diagram)

说明：  《matlab 7辅助信号处理技术与应用》下载资料，是学习信号处理的理想的资料。( "matlab 7 Cueing signals Processing Technology And Application" download the material, is studies the signal processing the ideal material. )

通过matlab语言编程，实现利用Gauss消去法求解线性方程组(Through matlab language programming, using the Gauss elimination method for solving linear equations)

有借鉴意义哦，BP神经网络的整个训练过程,空间目标识别，采用PM算法。( There are reference Oh, The entire training process BP neural network, Space target recognition algorithm using PM.)

Modelsim和Simulink协同仿真，很经典！！！！！(Modelsim Simulink)

RSSI仿真定位算法 ，使用RSSI算法进行定位( RSSI location algorithm simulation, using the RSSI algorithm to locate)

本书是《高质量c++编程指南》，林锐博士著，针对编程过程中可能会遇到的若干容易被忽略的问题进行了分析。(This book is "high-quality c++ Programming Guide" Dr. Lin Rui book for several problems easily overlooked programming process may encounter analyzed.)

Bandwidth (signal processing) or analog bandwidth, frequency bandwidth or radio bandwidth: a measure of the width of a range of frequencies, measured in hertz Bandwidth (computing) or digital bandwidth: a rate of data transfer, bit rate or throughput, measured in bits per second (bps) Spectral linewidth: the width of an atomic or molecular spectral line, measured in hertz

Matlab code for speech processing

Learning_random.m : 随机选择样例，从（90）pool里随机选择样本，删除版本空间树的数量 activeLearning.m:根据最大熵原则,从pool里选择样本，删除版本空间树的数量 getlabel.m:根据树和测试样例，得到根据该树得到的类标 getTrees.m:从提供的大量树结构(随机生成的)中，随机挑选一定数量的树(如果该树的叶子节点无类标随机添加) RandomCreateTree_d_unbalance:根据样本点割点中的非平衡割点建造树， RandomCreateTree_d_all.m:根据所有样本点的割点建造树 randomdata.m：给定属性取值，造数据 randomselect.m:从数据中随机选择一部分作为 showTree.m:显示树的结构 test.m:给出树，测试样例，得到正确率(Learning_random.m: randomly selected sample, randomly selected sample from (90) pool the The deleted version space tree quantity activeLearning.m: selecting a sample from the pool based on the principle of maximum entropy, delete the number of version space tree getlabel.m: According to the tree and the test sample obtained according to the class standard getTrees.m the tree: from the tree structure (randomly generated), randomly selected a certain number of trees (the leaves of the tree node class marked randomly adding ) RandomCreateTree_d_unbalance: According to the sample point cut point unbalanced cut point construction tree, RandomCreateTree_d_all.m: construction of the tree randomdata.m all sample points cut point: given the value of the property, manufacturing data randomselect.m: random data Select as part showTree.m: tree structure test.m: tree, the test sample is given to get the correct rate)