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In this article, the editor introduces "what is CRFs" in detail, the content is detailed, the steps are clear, and the details are handled properly. I hope this "what is CRFs" article can help you solve your doubts.

How does CRF solve the problems faced by graph models?

One way to solve this problem is to model the conditional distribution directly, which is all the classification needs. CRFs is essentially a method that combines the advantages of classification and graph model, combining the ability of compact modeling of multivariate data with the ability to predict using a large number of input features. The advantage of the conditional model is that the dependency relationship which only involves the input data variables does not work in the conditional model, so the structure of the accurate conditional model can be much simpler than the joint model. For machine learning geeks, the difference between generation model and CRFs is similar to that between naive Bayesian classifier and logical regression classifier. In fact, the polynomial logical regression model can be seen as the simplest CRF with only one output variable.

What is CRFs?

Conditional random field is a probabilistic framework for marking and segmenting structured data, such as sequences, trees and grids. This is particularly useful when modeling time series data, where time dependencies can be expressed in a variety of different forms. The basic idea is to define the conditional probability distribution on the tag sequence, rather than the joint distribution on the tag and observation sequence, given a specific observation sequence. The main advantage of CRFs is the relaxation of the independence assumption. The independent hypothesis means that variables do not depend on each other and do not influence each other in any way. This is not always the case, which can lead to serious mistakes.

HMM vs CRF

HMM is a generation model, which directly gives the output by modeling the transformation matrix based on the training data. You can improve the results by providing more data points, but you cannot directly control the output label. HMM learns the conversion probability based on the training data provided. Therefore, if we provide more data points, then we can improve the model to include a wider range of categories. CRF is a discriminant model that outputs a confidence level. In most cases, this is very useful because we want to know how certain the model is about the label at that point. This confidence can be threshold to adapt to a variety of applications. The advantage of confidence is that the number of false alarms is lower than that of HMM.

Compared with HMMs, the main advantage of CRFs is their conditional probability, which relaxes the independence assumption required by HMMs. In addition, CRFs avoids the problem of label deviation, which is the weakness shown by the Markov model based on the directed graph model. CRF can be seen as a generalization of HMM, or we can say that HMM is a special case of CRF, in which case constant probability is used to model state transitions. CRFs is superior to HMMs in many actual sequence tagging tasks.

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