Which of the following is a discriminative classifier?

Which of the following is a discriminative classifier?

Discriminative Classifiers learn what the features in the input are most useful to distinguish between the various possible classes. An example of a discriminative classifier is logistic regression. Mathematically, it directly calculates the posterior probability P(y|x) or learn a direct map from input x to label y.

What is discriminative probabilistic model?

The discriminative model is used particularly for supervised machine learning. Also called a conditional model, it learns the boundaries between classes or labels in a dataset. It creates new instances using probability estimates and maximum likelihood.

Which algorithm is discriminative in nature?

Logistic regression, SVM, and tree based classifiers (e.g. decision tree) are examples of discriminative classifiers. A discriminative model directly learns the conditional probability distribution P(y|x).

Is GMM generative or discriminative?

Generative / nonparametric: GMM which learns Gaussian distribution and have unfixed amount of parameters (latent parameters increases depending on the sample size) Generative / parametric: various Bayes based model. Discriminative / parametric: GLM, LDA and logistic regression.

Is Lstm discriminative or generative?

Employing the long short-term memory (LSTM) structure, we develop a discriminative model based on the hidden state and a generative model based on the cell state.

Is K means discriminative or generative?

It is generally acknowledged that discriminative objective functions (e.g., those based on the mutual information or the KL divergence) are more flexible than generative approaches (e.g., K-means) in the sense that they make fewer assumptions about the data distributions and, typically, yield much better unsupervised …

Is naive Bayes discriminative or generative?

Naive bayes is a Generative model whereas Logistic Regression is a Discriminative model . Generative model is based on the joint probability, p( x, y), of the inputs x and the label y, and make their predictions by using Bayes rules to calculate p(y | x), and then picking the most likely label y.

What is difference between generative and discriminative model?

Discriminative models draw boundaries in the data space, while generative models try to model how data is placed throughout the space. A generative model focuses on explaining how the data was generated, while a discriminative model focuses on predicting the labels of the data.

How is a discriminative model used in regression?

Discriminative model. Discriminative models, also referred to as conditional models, are a class of logistical models used for classification or regression. They distinguish decision boundaries through observed data, such as pass/fail, win/lose, alive/dead or healthy/sick.

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.

How are discriminative models used in data mining?

data mining. Discriminative models, also referred to as conditional models, are a class of models used in statistical classification, especially in supervised machine learning. A discriminative classifier tries to model by just depending on the observed data while learning how to do the classification from the given statistics.

How is a conditional model different from a discriminative model?

A conditional model models the conditional probability distribution, while the traditional discriminative model aims to optimize on mapping the input around the most similar trained samples. . to simulate the behavior of what we observed from the training data-set by the linear classifier method. Using the joint feature vector . Then the