模式识别 下载 pdb txt pdf 百度云 kindle umd 夸克云

本书的主要特点：

　　最新的特征生成技术，包括基于小波。小波包，分形的特征，还阐述了***分量分析。

　　新增了关子支持向量机，变形模板匹配的章节，以及关于约束优化的附录。

　　特征选择技术。

　　线性以及非线性分类器的设计，包括贝叶斯分类器、多层感知器，决策树和RBF网络。

　　***于上下文的分类，包括动态规划和隐马尔科夫建模技术。

　　不仅介绍了聚类算法的最新发展，而且还介绍了一些经典方法，诸如模糊。基因、退火等算法技术。

　　各种应用，包括图像分析。字符识别，医学诊断。语音识别以及信道均衡。

暂无相关目录，正在全力查找中！

暂无相关内容，正在全力查找中

暂无出版社相关信息，正在全力查找中！

暂无相关书籍摘录，正在全力查找中！

在线阅读地址：模式识别在线阅读

在线听书地址：模式识别在线收听

在线购买地址：模式识别在线购买

A general principle adopted by most of these techniques is the decomposition of the problem into smaller problems that are easier to handle.

For each smaller problem, a single node is employed. Its parameters are determined either i***tively using appropriate learning algorithms.

From the way these algorithms build the network, they are sometimes referred to as c***tructive techniques.

The other direction we will follow to design a multilayer perceptron is to fix the architecture and compute its synaptic parameters so as to minimize an appropriate cost function of its output.

This method not only overcomes the drawback of the resulting large networks of the previous section but also makes these networks powerful tools for a number of other applicati***, beyond pattern recognition.

However, this method is confronted with a serious difficulty. This is discontinuity of the step(activation) function, prohibiting differentiation with respect to the unknown parameters(synaptic weights).

A popluar family of continuous differentiable functi***, which approximate the step function, is the family of sigmoid functi***. A typical representative is the logistic function:

f(x) = frac{1}{1+exp(-ax)}

All these functi*** are also known as squashing functi*** since their output is limited in a finite range of values.

From the moment we move away from the step function, all we have said before about mapping the input vectors onto the vertices of a unit hyper-cube is no longer valid. It is now the cost function that takes on the burden for correct classification.

书籍介绍

本书的主要特点：

　　最新的特征生成技术，包括基于小波。小波包，分形的特征，还阐述了***分量分析。

　　新增了关子支持向量机，变形模板匹配的章节，以及关于约束优化的附录。

　　特征选择技术。

　　线性以及非线性分类器的设计，包括贝叶斯分类器、多层感知器，决策树和RBF网络。

　　***于上下文的分类，包括动态规划和隐马尔科夫建模技术。

　　不仅介绍了聚类算法的最新发展，而且还介绍了一些经典方法，诸如模糊。基因、退火等算法技术。

　　各种应用，包括图像分析。字符识别，医学诊断。语音识别以及信道均衡。