haoawesome
commented
10 years ago 概念
http://en.wikipedia.org/wiki/Conditional_probability The conditional probability of an event is the probability that the event will happen given that some other event has already occurred.
http://en.wikipedia.org/wiki/Graphical_model A graphical model is a probabilistic model for which a graph denotes the conditional dependence structure between random variables. They are commonly used in probability theory, statistics—particularly Bayesian statistics—and machine learning.
http://en.wikipedia.org/wiki/Bayesian_network A Bayesian network, Bayes network, belief network, Bayes(ian) model or probabilistic directed acyclic graphical model is a probabilistic graphical model (a type of statistical model) that represents a set of random variables and their conditional dependencies via a directed acyclic graph (DAG). For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases.
http://en.wikipedia.org/wiki/Markov_random_field In the domain of physics and probability, a Markov random field (often abbreviated as MRF), Markov network or undirected graphical model is a set of random variables having a Markov property described by an undirected graph.
条件随机场 http://en.wikipedia.org/wiki/Conditional_random_field Conditional random fields (CRFs) are a class of statistical modelling method often applied in pattern recognition and machine learning, where they are used for structured prediction. Whereas an ordinary classifier predicts a label for a single sample without regard to "neighboring" samples, a CRF can take context into account; e.g., the linear chain CRF popular in natural language processing predicts sequences of labels for sequences of input samples.
http://en.wikipedia.org/wiki/Decision_tree A decision tree is a decision support tool that uses a tree-like graph or model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. It is one way to display an algorithm.
我的问题是关于分类模型（classification model / framework）的，有时候可以对同一样本提取多种不同特征，一般来说，我们都假设特征之间是统计独立的，然后利用这些特征去做分类。
但我现在需要考虑的情况是，两个特征之间是非独立的，也就是说，其中一个特征是基于另一个特征所提供的信息而提取得到的，比如，在一幅图像中，先获取物体的形状（shape）或轮廓，再得到其中的外貌（appearance）特征。我想知道的是，有没有一些相关的文章或工作，是将特征间这种条件关系放入某种概率模型中来做分类的。谢谢你们！