Machine Learning Series - Introduction (b) of formula / discriminant model

1, machine learning tasks

• Machine learning task is to predict the mark from the property, namely determination of probability

 

 

2, discriminant model

• For example no, discriminant model can be obtained in accordance with markers, which can be directly determined out
• For dichotomous tasks, actually get a score, when the score is greater than the threshold, compared with positive class, otherwise it is a like a
• As shown in FIG left, is actually directly determined the boundaries
• Discriminant model is called "discrimination" model, because it is based on "discrimination"
• Common discriminant model
  • Linear regression models
  • Support vector machine ( SVM )
  • Logistic regression ( LR )
  • Neural Networks ( NN )
  • Gaussian process ( Gaussian Process )
  • Conditional Random CRF
  • CART （Classification and regression tree）
  • Boosting

 

3, the model formula

• For example no, need to model formula is obtained between the joint probability distribution of different labels, the probability of winning big
• As shown in the right side of FIG, and the absence of any boundary, for example no (red triangles), the sum of two joint probability distribution (two classes), compare, as a predicted high probability category
• Generation model is called "generation" model, because it is based on joint probability forecast
  • The joint probability can be interpreted as the probability of the "Generate" distribution of the sample (or called basis)
• Specifically,
  • Known machine learning, from a set of candidate selected from one of
  • The sample may have, (the X-| Y_2) , (the X-| Y_3) , ...... ,
  • The actual data is how to "generate" depends on
  • Then the final results of the election predicted "generate" the greatest probability that
• Familiar Naive Bayes all know, for input, you need to obtain several joint probability, then the larger one is to predict results
• Common generative model
  • Discriminant analysis: Gaussian discriminant model
  • Naive Bayes ( Naive Bayes )
  • Gaussian mixture model ( Gaussians )
  • K neighbors ( KNN )
  • Hidden Markov model ( the HMM )
  • Bayesian networks
  • Sigmoid belief networks ( S igmoid Belief Networks )
  • MRF ( Markov Random Fields )
  • Deep belief network DBN
  • Latent Dirichlet Allocation ( LDA , Latent Dirichlet Allocation )
  • Multi-expert model ( at The Mixture of Experts Model )

 

4, analog Case

• Determine a sheep or goat sheep
• Discriminant model:
  • Learning from historical data to model
  • This feature extraction sheep to predict this sheep is a sheep or goat
  • That can give the sheep a sheep directly from the probability of feature
• Generative model:
  • According to first learn the characteristics of goat model of a goat
  • It features a learning model sheep sheep
  • Extracting features from sheep, a goat model into what is the probability seen, put sheep seen what is the probability model
  • That is a big probability model class sheep belongs to this category
  • That is, the generative model for each class to have a try, that is, the last resulting most probable

 

 

Reference blog:

https://www.zhihu.com/question/20446337 (machine learning "model determination," and "generative model" What is the difference)

https://blog.csdn.net/u010358304/article/details/79748153 (Model VS generated discriminant model)

https://www.nowcoder.com/questionTerminal/e7ac0572b29a490da333d2c7ff8623ac?orderByHotValue=0&done=0&pos=1&mutiTagIds=631&onlyReference=false (discriminant model formula model)