Pattern recognition 模式识别

The foundations of pattern recognition can be traced to Plato, which were later extended by Aristotle, who distinguished between an "essential property" (which would be shared by all members in a class or “natural kind” as he put it) from an “accidental property” (which could differ among members in the class). Pattern recognition can be cast as the problem of finding such essential properties of a category. It has been a central theme in the discipline of philosophical epistemology, the study of the nature of knowledge.

有关模式识别基础的讨论最早可追溯到柏拉图，进而被亚里士多德所发展。亚里士多德将事物的性质区分为"本质属性"（指某一类或他称之为"自然属性"(natural kind)的所有成员的共同性质）和"例外属性"(accidental property)（指类中成员之间的不同性质）。而模式识别的任务就是找出某’类’事物的"本质属性"。这也是哲学中认识论所研究的中心问题，即，对知识本质的研究。

maximum likelihood estimation and Bayesian estimation 最大似然估计和贝叶斯估计

Maximum likelihood and several other methods view the parameters as quantities whose values are fixed but unknown. The best estimate of their value is defined to be the one that maximizes the probability of obtaining
the samples actually observed. In contrast, Bayesian methods view the parameters as random variables having some known a priori distribution. Observation of the samples converts this to a posterior density, thereby revising our opinion about the true values of the parameters. In the Bayesian case, we shall see that a typical effect of observing additional samples is to sharpen the a posteriori density function, causing it to peak near the true values of the parameters. This phenomenon is known as Bayesian learning.

最大似然估计（和其他的一些类似方法）把待估计的参数看作是确定性的量，只是其取值未知。最佳估计就是使得产生已观测到样本（即训练样本）的概率为最大值。与此不同的是，贝叶斯估计则把待估计的参数看成是符合某种先验概率分布的随机变量。对样本进行观测的过程，就是把先验概率的参数转化为后验概率密度，这样就利用样本的信息修正了对参数的初始估计值。在贝叶斯估计中，一个典型的效果就是，每得到新的观测样本，都使得后验概率密度函数变得更加尖锐，使其在待估参数的真实值附近形成最大的尖峰。这个现象就称为“贝叶斯学习”过程

supervised learning and unsupervised learning 有监督学习和无监督学习

In both cases, samples x are assumed to be obtained by selecting a state of nature with probability P(), and then independently selecting x according to the probability law p(x|). The distinction is that with supervised learning we know the state of nature (class label) for each sample, whereas with unsupervised learning we do not.