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Is GMM a discriminative model?
Example models: Generative / nonparametric: GMM which learns Gaussian distribution and have unfixed amount of parameters (latent parameters increases depending on the sample size) Discriminative / nonparametric: KNN, SVM, K-Mean and CART.
Is logistic regression a discriminative model?
Generative and discriminative models are widely used machine learning models. For example, Logistic Regression, Support Vector Machine and Conditional Random Fields are popular discriminative models; Naive Bayes, Bayesian Networks and Hidden Markov models are commonly used generative models.
Is Bert a discriminative model?
BERT [4] obtained new state-of-the-art results on a broad range of diverse tasks. Unlike previous works fine-tuning pre-trained language model to perform discriminative tasks, we aim to apply pre-trained BERT on generative tasks by perform the masked language model (MLM) task.
Which is an example of a discriminative model?
Examples of discriminative models in machine learning include support vector machines, logistic regression, decision trees, and random forests. Here’s a quick rundown of the major differences between generative and discriminative models. Generative models aim to capture the actual distribution of the classes in the dataset.
How are posterior probabilities obtained in a discriminative model?
In the discriminative model, the posterior probabilities, , is inferred from a parametric model, where the parameters come from the training data. Points of estimation of the parameters are obtained from the maximization of likelihood or distribution computation over the parameters.
How are generative and discriminative models used in PGMs?
To get the conditional probability P (Y|X), generative models estimate the prior P (Y) and likelihood P (X|Y) from training data and use Bayes rule to calculate the posterior P (Y |X): On the other hand, discriminative models directly assume functional form for P (Y|X) and estimate parameters of P (Y|X) directly from training data.
How are discriminative models used in machine learning?
Discriminative models, also referred to as conditional models or backward models, are a class of supervised machine learning used for classification or regression. These distinguish decision boundaries by inferring knowledge from observed data.